Interactive and Social Knowledge Discovery Sessions

ABSTRACT

There are provided methods and systems to assist interactively a knowledge user/contributor to obtain a straight answer to his/her request for knowledge about one or more subject matters, to mediates a large group of unknown inquirers and present them with distilled stage of knowledge related to a subject matter, or to guide and assist by providing knowledge graphs of bodies of knowledge and navigation tools, individually or socially, to find or discover credible and value significant knowledge based on value significance aspects and measures, at much faster rate than the current traditional method of using search engine directories, social networking, blogging, and bookmarking websites. Generally the presented systems, methods, formulations and algorithms, provide intelligent and knowledgeable agents which learn from bodies of knowledge and assist, converse, measuring the merit of users&#39; input, mediate and participate in a group discussions, provide and compose high value content in demand to users of such systems. The methods, systems and services of the presented disclosure significantly increases productivity of knowledge-based users, the quality of their work, and accelerating the rate of knowledge discoveries.

CROSS-REFERENCED TO RELATED APPLICATIONS

The present application is a continuation of and claims the benefit ofU.S. patent application Ser. No. 12/955,496, filed on Nov. 29, 2010,entitled “INTERACTIVE AND SOCIAL KNOWLEDGE DISCOVERY SESSIONS” whichclaims priority from U.S. provisional patent application No. 61/311.368filed on Mar. 7, 2010, entitled Interactive and Social KnowledgeDiscovery Sessions”, which also cross-references and claims the benefitsof the U.S. patent application entitled: “AUTOMATIC CONTENT COMPOSITIONGENERATION”, application Ser. No. 12/946,838, now U.S. Pat. No.8,560,599 B2, filed on Nov. 15, 2010, which claims priority from U.S.provisional application No. 61/263,685 filed on Nov. 23, 2009, entitled“Automatic Content Composition Generation”; and

U.S. patent application Ser. No. 12/939,112, now U.S. Pat. No. 8,401,980B2 entitled “METHODS FOR DETERMINING CONTEXT OF COMPOSITIONS OFONTOLOGICAL SUBJECTS AND THE APPLICATIONS THEREOF USING VALUESIGNIFICANCE MEASURES (VSMS), CO-OCCURANCES AND FREQUENCY OF OCCURANCESOF THE ONTOLOGICAL SUBJBJECTS SYSTEM”, filed on Nov., 3, 2010, whichclaims priority from U.S. provisional application No. 61/259,640 filedon Nov. 10, 2009, entitled “SYSTEM AND METHOD FOR VALUE SIGNIFICANCEEVALUATION OF ONTOLOGICAL SUBJECTS OF NETWORKS AND THE APPLICATIONTHEREOF”; and

U.S. patent application Ser. No. 12/908,856 entitled “SYSTEM AND METHODOF CONTENT GENERATION”, filed on Oct. 20, 2010, which claims priorityfrom U.S. provisional application No. 61/253,511 filed on Oct. 21, 2009,entitled “System and Method of Content Generation”; and

U.S. patent application Ser. No. 12/755,415 entitled “SYSTEM AND METHODFOR A UNIFIED SEMANTIC RANKING OF COMPOSITIONS OF ONTOLOGICAL SUBJECTSAND THE APPLICATIONS THEREOF”, now U.S. Pat. No. 8,612,445 B2, filed onApr. 7, 2010, which claims priority from U.S. provisional patentapplication No. 61/177,696 filed on May 13, 2009 entitled: “System andMethod for a Unified Semantic Ranking of Compositions of OntologicalSubjects and the Applications Thereof”; and

U.S. patent application Ser. No. 12/547,879 entitled “SYSTEM AND METHODOF ONTOLOGICAL SUBJECT MAPPING FOR KNOWLEDGE PROCESSING APPLICATIONS”,now U.S. Pat. No. 8,452,725 B2 filed on Aug. 26, 2009, which claimspriority from U.S. provisional application No. 61/093,952 filed on Sep.3, 2008, entitled “System And Method Of Ontological Subject Mapping ForKnowledge Processing Applications”; and

U.S. patent application Ser. No. 12/179,363 entitled “ASSISTED KNOWLEDGEDISCOVERY AND PUBLICATION SYSTEM AND METHOD”, filed on Jul. 24, 2008,which claims priority from Canadian Patent Application Ser. No CA2,595,541, filed on Jul. 26, 2007, entitled “Assisted KnowledgeDiscovery and Publication System and Method”; which are all hereinincorporated by reference in their entirety for all purposes.

FIELD OF INVENTION

This invention generally relates to interactive and social knowledgediscovery and representation, information processing, ontologicalsubject processing, knowledge processing and discovery, knowledgeretrieval, artificial intelligence, information theory, natural languageprocessing and the applications.

BACKGROUND OF THE INVENTION

Currently a researcher or information seeker usually use a search engineto get a list of compositions that potentially can provide an answer orassist the researcher to get a better understanding of her/his subjectmatter of interest and help the user in his/her challenge. As widelybeen experienced this exercise is not very efficient and take a lot oftime and requires lots of skills for a researcher. The users still haveto sift through countless pages to find out the answer.

Social networks on the other hand provide a way of content sharing butdo not assist the users, for knowledge acquisition and discovery, beyondsharing.

Therefore current search engines or social networks' services are notsufficiently efficient for knowledge discoveries, and even sometimes aremisleading for knowledge seekers and professional researchers as well asgeneral public.

SUMMARY OF THE INVENTION

One object of the present application is to find out and address thedrawbacks with the current stages of information retrieval and knowledgeacquisition/discovery and the overlooked potentials of the search engineand social networking services. The present application consequentlywill disclose technologies, methods, and systems without thoseshortcomings and provide a number of novel applications and services.

The present application discloses systems and methods of interactive andsocial knowledge discovery and new services to assist the users orclients in finding, discovering, brainstorming, and creating qualifiedor relevant knowledge of interest and importance, interactively and/orsocially.

It has been noticed, in this application, that at any given time a largenumber of people are searching and exploring for the same subjectmatters by querying and connecting to search engines. The currentsystems and methods of search engines do not have the capability tocapitalize on this opportunity to simultaneously connect these diversegroups of people commonly looking for specific knowledge. Socialnetworking websites, blogger, bookmarking services and the like, whileconnecting people and friends, do not provide the desired service sincepeople are instructed to loggings and only have access to a selectedgroup of people and discussions. This decreases the chances of meetinglikeminded people if they did not know each other before. Moreover, thesocial networking websites and services are not geared toward finding,distilling, and acquiring knowledge since they do not have automaticmediating tools to present the distilled stage of knowledge about asubject matter to its users and visitors.

If the users, unknown to each other, could communicate, through anautomatic mediator, with each other while exploring and searching forknowledge about a subject, then this new scheme of knowledgeexploration, discovery, and knowledge distillations will find a fasterpace and more problems can be solved in less time leading to economicalas well cultural and personal growth of the society and human being as awhole.

Moreover, for instance, consider an ordinary searcher or a professionalknowledge worker who need or is assigned to gain information or toobtain knowledge about a subject matter. However, for any topic orsubject matter, there are vast amount of repositories such as collectionof research papers, news feeds, interviews, talks, lectures, books,advertisements, twitters short messages, multimedia content, videos andthe like. One needs lots of expertise, time, and many years of trainingto benefit from such unstructured collections of information in order tofind out the knowledge that he is looking for or make a contribution toadvance the state of the knowledge.

Also very often a user is only looking for a quick fact or a verifiedpiece of information about something, and because of that the user hasto spend considerable amount of time to find the correct and usefulinformation. Nevertheless, still the user cannot be sure that howcredible and reliable the found information is. Sometimes on the otherhand a user would like to find novel information about something that isless known or less quoted or is hidden inside a long website or severalless observed webpages or compositions.

In order to speed up the process of such a research and due diligencesit is important to identify the role of each concept, any force, andtheir relations in the desired system of knowledge. By the system ofknowledge we mean a Body Of Knowledge (sometimes called BOK hereinafter)in any field, narrow or wide. For instance a system of knowledge or aBOK can be defined about an individual or an enterprise entity or anyscientific subject matter. In these exemplary cases, there are manyunknowns that are desired to be known. So consider someone has collectedmany or all textual compositions about a subject. Apparently thecollection contains many useful pieces of information about the subjectthat are important but can easily be overlooked by a human due to thelimitations of processing capability and memory capacity of theindividual's brain.

Accordingly, one aspect of the present application introduces systems,methods and services that assist the information seeker/s interactively.The system provides a straight answer to the client question, or queriesaccording to the latest stage of knowledge in the form of various typesof services that the client may demand.

For example, in one exemplary embodiment, the user only provide akeyword and asking about the most credible fact or statement related tothe keyword or the query and the system and method of the presentinvention will start an interactive searching or knowledge discoverysession. The system will assemble a body of knowledge, using either itsown databases or other search engines or any other means, related to theuser's query or subject matter. Using the method of the referencedpatent applications the system partitions and evaluates the significanceof each partitions of the BOK by calculating the value significancemeasures (VSMx, x=1, 2 . . . ) of the partitions of the BOK. Thepartitions of the BOK can be simply the words and phrases, sentences,paragraphs, pages, and whole document or a webpage. Having calculatedthe VSMs of the partitions then the system can provide the appropriateanswer or response to the request for knowledge back to the user.Usually the answer contains those partitions, e.g. sentences orparagraphs, of the BOK that have scored the highest VSMs and containsthe requested subject matter/s or other associates of the subject matterfound in BOK. However, the answer also could be the webpages or thewhole document that have scored high. If the user asking for novelinformation or knowledge about a subject matter, that can also be foundin the BOK, the interactive knowledge discovery session follows themethods of the patent application 12/939,112 and select the appropriatetype of VSM for scoring the partitions for that service and return orprovide the response accordingly.

In another instance and according to one exemplary embodiment of thisinvention the system therefore will provide an overall credible summaryaccording to the state of the knowledge about the query or the subjectmatter in the context of the BOK, using the content of the BOK, and getback to the user.

In yet another exemplary embodiment, the session provides a concisesummary in the form of bulleted presentation which makes it easier tograsp the context and the most important knowable parts about thesubject matter. Each of the bulleted statement states one of the mostcredible facts about an important aspect of the subject matter. Moreoverthe presentation can have the option and capability for being pointed bythe searcher and get more comprehensive credible information about thestatement. By credible here we mean the most valuable partitions of thecontents of the BOK as were defined and can be calculated using theteachings of the reference patent application Ser. Nos. 12/755,415 and12/939,112.

In another instance consider that the BOK consists of a plurality ofnews feed, which are usually very redundant, then the system and methodintroduced in this invention provide the user with the most importantand credible pieces of the news while the user or the client can be surethat he/she has found knowledge of the most important parts of the newswithout worrying about missing the most important information containedin the news.

In another exemplary embodiment, the system provides graphs that can beused as cognitive maps to visually and quickly grasp the context ofsubject matter's BOK. In fact, the system will provide a backbone graphindicating the relationships between the concepts and entities of theBOK and therefore visualizes the true context of the BOK and thereforethe context of the universe of the body of knowledge is revealed. Agraphical user interface GUI) is further devised that a user can use bypointing on a node/s and/or edge/s of the knowledge map in order to getthe most credible content found in the body of knowledge related to thatnode or the nodes connected by the pointed edge. In this way the usercan quickly navigates the most important knowable about the subjectmatter and help the user to reason further and to reach his/her ownconclusions about other aspects of the subject matter.

Further, the user then will be provided with environments to ask furtherand/or more specific question and the system adaptively andinteractively provides the answer found from the assembled body ofknowledge in relation to the user's subject matter of interest. The useragain can ask more specific questions and the system will provide morefurther detailed information in response to the latest user's questionor request. The system effectively will act as an expert knowledgeconsultant to the user interactively. The system moreover keeps track ofthe exploration and provides the trajectory with the highest valuedpartitions of the information in each stage of the explorationtrajectory. In this way the searcher and the system participate andcollaborate to narrow down the relations and/or find the best researchpath or finding/discovering the logical relations between theontological subjects (e.g. subject matters) of the interest containedand used n the BOK.

Among the many advantages of the presented system and method of theknowledge discovery is that even a less known website that have oneextremely valuable piece of information will be seen in the searchingsession. Therefore if a webpage has even one wining partitions it willmake it to the top results and will have better chance of being seen andnoticed. The system is therefore fairer giving the user the bestexposure to valuable contents while it also give the service providervendor the capability of soliciting more target advertiser if desired bythe service provider.

In another embodiment, additionally the system and the client discovernew relations between ontological subjects (OSs) that were not known orwere less known and the user can add or edit this new information to thesystem with human edition. Since the interactive searching andexploration session is challenging and fun therefore many people canparticipate simultaneously or non-simultaneously. There could further bea prize to find out or guess or reasoning a new knowledge so that peoplewill be more motivated to use the system and as a result add new or morepolished knowledge.

Also more importantly, it is noticed here that at any given time a largenumber of people are searching and exploring for the same subjectmatters by querying and connecting to search engines. If the unknown toeach other users, could communicate, through an automatic mediator, witheach other while exploring and searching for knowledge about a subject,then this new scheme of knowledge exploration, discovery, and knowledgedistillations will find a faster pace and more problems can be solved inless time leading to economical as well as cultural and personal growthof the society and human being as a whole.

Accordingly, in another aspect of the present application embodimentsare given wherein the interactive searching and exploration session orquestion answering, can be taken simultaneously with other clients thatare searching or looking for the knowledge about a common subjectmatter. In this way we have an interactive and social assisted knowledgediscovery session to proliferate further knowledge discoveries. Thequestions from user and the answers given by the system can be exchangedin the multimedia forms. For instance the client can ask a question bytext or audio and receive the answer in the form of a text or audio orother multimedia forms.

Therefore, in yet another embodiment according to the methods ofevaluating the value of compositions as described and disclosed in theincorporated patent application Ser. No. 12/755,415 and 12/939,112,there is provided an interactive searching service that once a userquires the systems about a subject matter the user or the client isguided to an open session that is shared with other users or clientsthat were looking for knowledge about the same subject matter, and thenew user can quickly get an update on the latest findings and the bestpieces of information or knowledge found in the respective BOK of thesubject matter. The new participant therefore can also join theinteractive and social knowledge discovery session and start to gaininstant updated knowledge or contribute to the BOK of that session.However since the system is capable of interacting with the user thesystem itself can be viewed as an active participant and therefore thesocial interactive knowledge discovery session can always be formed evenif there is only one human participant. Although some of theparticipants might be software agents that are looking to find theinformation for their own clients.

In the case of social exploration the system can always provide the mostupdated and well-rounded answer to the participants while the systemitself can act as a participant in the social session. The systemfurther aggregate the participants contributions and distill thecontributions and show the stage of knowledge about the subject matterof the session and its associates subjects matters up to the second andalso show the exploring and discovery trajectory taken in that session.The session can be closed or stayed open indefinitely either by thesystem or by the client/user.

In the social exploration session the system can also give an instantfeedback to the participants and bring the latest most valuable relatedinformation to the participant contribution or statement or question.Also a good question can be rewarded based on the value and thegenerated knowledge as a result of the question or the proposedstatement by measuring the significance value of the generated knowledgeas a result of the user's question or proposal.

The number of participants can be very large and the system provides thelatest founding about the subject matter of the interest to eachparticipant. In this case the system will act as a mediator. Theparticipants can be the registered users competing with each other toprovide a higher value contribution thereby giving the people theincentive and motivation to participate. The system can provide theincentive to the contributing participant in the form of credit ormonetary valuable scores, notes, etc.

Third party can provide further incentives for knowledge discoverysessions. For instance an enterprise can introduce a prize or incentiveto the contributors of knowledge discovery sessions related to thesubject matters that are important for that enterprise. The system isable to measure the significance of contributions again using thetechnology and system and method disclosed the referenced patentapplications.

In another application consider that a user have collected a number ofdocuments and contents and would like to search within that collectionor body of knowledge (BOK). The current keyword searching methods alonewill not work here since the collection might be large and for any givenkeyword, especially for the dominant keywords of the BOK, there will befound many statements or partitions that contain the keyword but mightnot have any real knowledge significance or informational value. Thepresented system and method here along with the methods and teachings ofthe referenced patent applications always presents the most significantpartitions of the BOK in response to a query from user for finding theinformation from the BOK. Again the system moreover will provide abackbone graph indicating the relationships between the concepts andentities of the BOK and therefore visualizes the true context of the BOKand therefore the context of the universe of the body of knowledge isrevealed.

One application of such embodiments beside individual users, as anindividual researcher or knowledge seeker or student or trainee, is thatlarge number of people can participate to produce new knowledge orcompose a new and more valuable composition. For instance editorialarticles can be added to the knowledge database. The content further canbe shared or published in one of the publishing shops (as was introducedin the published US patent application US 200930030897 filed by the sameapplicant) or other media.

Therefore in yet another embodiment a user can create his own journaland submit and solicit contents, the system then assemble a BOK (with orwithout the help of the user or other users) for that subject mattersubmitted by the user. There could be many sorts of arrangements betweenthe vendor executing the methods of this invention and a user forestablishing a journal. For instance, if the user's content rank in topten list of the most valuable contents in the context of the assembledBOK then user have the option to claim that journal (in accordance withthe incorporated reference the patent application Ser. No. 12/179, 363,Pub. No. US 2009/0030897) and enjoys the benefits of the journal such asad revenue, paid research etc. However still other people can compete togenerate other journals on the same subject matter if they becomequalifies (their submitted content ranks top ten in the context of theassembled BOK related to the subject matter)

However, in yet another embodiment, a client and user start a sessionfor automatic and interactive content multimedia generation. The contentcould also be a multimedia content (as explained in the provisionalpatent application 61/253,5114 filed on Oct. 21, 2009 and the patentapplication 61/263,685 filed on Nov. 23, 2009, now U.S. Pat. No.8,560,599 B2) and interactively edit the user's generated multimediacontent until he/she is satisfied and perhaps would like to share thecontent with others in the publishing or broadcasting shops or YouTubeand/or the like.

Consequently, the disclosed system/s and method/s can assist a knowledgeuser/contributor to obtain a straight answer to his/her request forknowledge about one or more subject matter, can mediates a large groupof unknown inquirers and present them with distilled stage of knowledgerelated to a subject matter, and/or can guide and assist, individuallyor socially, to find or discover credible value significant knowledge atmuch faster rate than the current traditional method of using searchengine directories, social networking, blogging, and bookmarkingwebsites. Such a system and method will increase significantly theproductivity and quality of the works of knowledge-based works as wellas general public.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: shows one exemplary flow diagram illustration of the InteractiveKnowledge Discovery Session (IKDS) system.

FIG. 2 a: shows one exemplary result of the IKDS in response to theuser/s request for information in which the knowledge about a subjectmatter is represented in the form of shortest most credible statementsfound in the assembled Body Of Knowledge (BOK).

FIG. 2 b: shows another exemplary result of the IKDS in response to theuser/s request for information in which the knowledge about a subjectmatter is represented in the form of listed most credible statementsfound in the assembled Body Of Knowledge (BOK) related to the requestedsubject matter in which further user's interfaces are provided forbetter navigation through a multipage output and more optionalrepresentation modes.

FIG. 3 a and b: show other exemplary outputs of the IKDS in response tothe user/s request for knowledge about a subject matter in the form of amultilayer map in which the most significant subjects associated withthe main subject matter are mapped according to the present invention.FIG. 3 a in the form of tree and 3 b is the free form graph or map withthe queried subject matter SM0 in the middle.

FIG. 4 a: shows an exemplary way of navigating over the map and gettingthe most credible partitions of the BOK contains the selected subjectmatters (nodes) in the map by pointing on the edges of the graph.

FIG. 4 b: shows another exemplary way of navigating over the map andgetting the most credible partitions of the BOK contains the selectedsubject matters (nodes) in the map by pointing and confirming the nodesfor which the information is sought.

FIG. 4 c: shows another exemplary way of navigating over the map andgetting the most credible partitions of the BOK contains the selectedsubject matters (nodes) in the map by drawing and defining an areas ofthe map for which the knowledge is sought about.

FIG. 5: shows an exemplary way of illustrating the knowledge map for therequested subject matter in which the nodes are placed based on theirassociation strength thereby visually demonstrating the closeness andsignificance of each node to each there in the context of the BOKassembled for a subject matter.

FIG. 6: shows one exemplary embodiment of a “Interactive/SocialKnowledge Discovery Sessions (ISKDS)” display with online participants

FIG. 7: shows another exemplary embodiment of an ISKDS with onlineparticipants wherein the system monitors and gives the most updatedpiece of knowledge about the subject for the participants to see.

FIG. 8: shows another exemplary embodiment of an ISKDS with onlineparticipants wherein the system monitor and give the most updated pieceof knowledge about the subject for the participants to see which isfurther customized for each participant in which for example the usercan see the sketch of different forms of the knowledge representationabout the subject matters of the discussion.

FIG. 9: shows another exemplary social ISKDS wherein other ongoing oroffline ISKDS about the most significant associates of the subjectmatter or any other desired subject is displayed to the participant sothat the participant can switch back and forth between the sessions.

FIG. 10: the ISKDS system will score the input from the participants inthe context of the assembled BOK related to the subject matter.

FIG. 11: The ISKDS system in which the client provides the content,databases to build a BOK from or have a BOK for knowledge discoverysession.

FIG. 12: shows a block diagram of the publication system by the usersand clients using the services of the ISKDS.

DETAILED DESCRIPTION

Currently search engines do not provide further services besidespointing out to webpages and displaying a partition of the pages that akeyword has been appeared without any judgment on the importance of thatpartition. The default in current searching utilities is that if awebpage has high rank then the displayed partition should also have highquality. Moreover the need for more information will immediately ariseafter first finding of the desired knowledge. Many personal experienceswith search engine show that they are not helpful in assisting knowledgeseeker to find the right information in many occasions. In other wordssearch engines do not present the correct and sought after informationto the searcher but rather only points them to some potential (almostrandom looking order) places that one might find the answer that islooking for.

The problem might be due to the fact that there are so many websites anddocuments having good contents that the current searching enginealgorithms and services are not able to effectively find the best andthe most relevant information that one needs. This is more evident whensomeone is searching for information or knowledge about subjects thatpotentially hundreds of thousands or even millions of documents arefound by the search engine service providers.

Besides, even though the size of the Internet's content has growntremendously during the last decade, the look and technology of searchengines have remained effectively the same. Search engine servicesprovide ‘one size fits all’ response to people's queries by just showingthe users a reputable website that has mentioned the subject matter(i.e. the user query or part of it) which is even very often hard tofind the highlighted part in the pointed website or webpage as well. Thepartitions that are presented along with the ranked search result onlycontain the keywords of the query at the best and there is no guaranteethat these partitions are useful or have an intrinsic value or can helpthe user.

Furthermore, the current state of the art for a knowledge seeker and acontent composer is not fair and only works in favor of the brandedwebsites and webpages, which is both not healthy for knowledge discoverynor it is fair to individual knowledge contributors who do not haveaccess to the branded webpages for visibly publishing their work amongmany similar compositions. That is because so far search engines do noteffectively assess the value of compositions independent of thepublisher reputation and popularity. Branded web-publisher can have manycompositions for a single subject matter which makes it hard to find acontent or a part that can have really significant intrinsic value.

Also more importantly, one can notice that at any given time a largenumber of people are searching and exploring for the same subjectmatters by querying and connecting to search engines. The currentsystems and methods of search engines do not have the capability tocapitalize on this opportunity to simultaneously connect these diversegroups of people commonly looking for specific knowledge. Socialnetworking websites, blogger, bookmarking services and the like, whileconnecting people and friends, do not provide the desired service sincepeople are instructed to loggings and only have access to a selectedgroup of people and discussions. This decreases the chances of meetinglikeminded people if they did not know each other before. Moreover, thesocial networking websites and services are not geared toward finding,distilling, and acquiring knowledge since they do not have automaticmediating tools to present the distilled stage of knowledge about asubject matter to its users and visitors.

If the users, unknown to each other, could communicate, through anautomatic mediator, with each other while exploring and searching forknowledge about a subject, then this new scheme of knowledgeexploration, discovery, and knowledge distillations will find a fasterpace and more problems can be solved in less time leading to economicalas well cultural and personal growth of the society and human being as awhole.

Therefore, a system and/or method is desirable to present the pieces ofinformation and knowledge, based on their intrinsic significance orvalues in the context of a large body of knowledge, which is lessdependable on the popularity, brand and reputation of the publisher.Moreover it is very desirable to have a system and/or method that couldprovide the correct and verified information on demand and have thecapability to accompany and assist the users toward finding or creatingthe credible answer and contents in his/her knowledge explorationjourney. Also importantly, it is very desirable to have a system andmethod of knowledge exchange and discovery session for users who areseeking and exploring common subject matter/s.

Consequently, there is a need for more advanced system/s and method/sthat can assist a knowledge user/contributor to obtain a straight answerto his/her request for knowledge about one or more subject matter, canmediates a large group of unknown inquirers and present them withdistilled stage of knowledge related to a subject matter, and/or canguide and assist, individually or socially, to find or discover crediblevalue significant knowledge at much faster rate than the currenttraditional method of using search engine directories, socialnetworking, blogging, and bookmarking websites. Such a system andmethod, which is disclosed herein, will increase significantly theproductivity and quality of the works of knowledge-based works as wellas general public.

The present detailed disclosure uses mostly the notions, definitions,variables, and the disclosed methods and algorithms from the patentapplication 12/755,415 entitled “System and Method For A UnifiedSemantic Ranking of Compositions of Ontological Subjects and theApplications Thereof” filed on Apr. 7, 2010 and the patent application12/939,112 entitled “System and Method of Value Significance Evaluationof Ontological Subjects of Networks and the Applications Thereof” filedon Nov. 3, 2010 by the same applicant.

In the patent application Ser. Nos. 12/755,415 and 12/939,112 methods,systems, and algorithms were disclosed to evaluate the significancevalue of ontological subjects and compositions of ontological subjectssuch as measuring the value significance of words, sentences,paragraphs, documents, or webpages in the context of a “Body ofKnowledge” as we sometimes call hereafter as BOK.

Accordingly, this disclosure uses the definitions that were introducedin the referenced applications and more particularly in the U.S. patentapplication Ser. Nos. 12/755,415 and 12/939,112 which are incorporatedas references. We also use some or all parts of the definitions and themethods and algorithms of those applications in performing the disclosedsystems and methods of “Interactive and Social Knowledge DiscoverySessions ISKDS” services. Accordingly some introductory parts of thoseapplications are recited here again along with more clarifying pointsaccording to their usage in this disclosure and the mathematicalformulations herein.

I—DEFINITIONS

1. Ontological Subject: symbol or signal referring to a thing (tangibleor otherwise) worthy of knowing about. Therefore Ontological Subjectmeans generally any string of characters, but more specifically,characters, letters, numbers, words, bits, mathematical functions, soundsignal tracks, video signal tracks, electrical signals, chemicalmolecules such as DNAs and their parts, or any combinations of them, andmore specifically all such string combinations that indicates or referto an entity, concept, quantity, and the incidences of such entities,concepts, and quantities. In this disclosure Ontological Subject's andthe abbreviation OS or OSs are used interchangeably.

2. Ordered Ontological subjects: Ontological Subjects can be dividedinto sets with different orders depends on their length, attribute, andfunction. For instance, for ontological subjects of textual nature, onemay characterizes letters as zeroth order OS, words as the first order,sentences as the second order, paragraphs as the third order, pages orchapters as the fourth order, documents as the fifth order, corpuses asthe sixth order OS and so on. So a higher order OS is a combination or aset of lower order OSs or lower order OSs are members of a higher orderOS. Equally one can order the genetic codes in different orders ofontological subjects. For instance, the 4 basis of a DNA molecules asthe zeroth order OS, the base pairs as the first order, sets of piecesof DNA as the second order, genes as the third order, chromosomes as thefourth order, genomes as the fifth order, sets of similar genomes as thesixth order, sets of sets of genomes as the seventh order and so on. Yetthe same can be defined for information bearing signals such as analogueand digital signals representing audio or video information. Forinstance for digital signals representing a video signal, bits(electrical One and Zero) can be defined as zeroth order OS, the bytesas first order, any sets of bytes as third order, and sets of sets ofbytes, e.g. a frame, as fourth order OS and so on. Therefore definitionsof orders for ontological subjects are arbitrary set of initialdefinitions that one should stick to in order to make sense of methodsand mathematical formulations presented here and being able to interpretthe consequent results or outcomes in more sensible and familiarlanguage.

More importantly Ontological Subjects can be stored, processed,manipulated, and transported only by transferring, transforming, andusing matter or energy (equivalent to matter) and hence the OSprocessing is a completely physical transformation of materials andenergy.

3. Composition: is an OS composed of constituent ontological subjects oflower or the same order, particularly text documents written in naturallanguage documents, genetic codes, encryption codes, data files, voicefiles, video files, and any mixture thereof. A collection, or a set, ofcompositions is also a composition. Therefore a composition is also anOntological Subject which can be broken to lower order constituentOntological Subjects. In this disclosure, the preferred exemplarycomposition is a set of data containing ontological subjects, forexample a webpage, papers, documents, books, a set of webpages, sets ofPDF articles, multimedia files, or simply words and phrases.Compositions are distinctly defined here for assisting the descriptionin more familiar language than a technical language using only thedefined OSs notations.

4. Partitions of a composition: a partition of a composition, ingeneral, is a part or whole, i.e. a subset, of a composition orcollection of compositions. Therefore, a partition is also anOntological Subject having the same or lower order than the compositionas an OS. More specifically in the case of textual compositions,partitions of a composition can be chosen to be characters, words,sentences, paragraphs, chapters, webpage, etc. A partition of acomposition is also any string of symbols representing any form ofinformation bearing signals such as audio or videos, texts, DNAmolecules, genetic letters, genes, and any combinations thereof. Howeverour preferred exemplary definition of a partition of a composition inthis disclosure is word, sentence, paragraph, page, chapters and thelike, or WebPages, and partitions of a collection of compositions canmoreover include one or more of the individual compositions. Partitionsare also distinctly defined here for assisting the description in morefamiliar language than a technical language using only the general OSsdefinitions.

5. Value Significance Measure: assigning a quantity, or a number orfeature or a metric for an OS from a set of OSs so as to assist theselection of one or more of the OSs from the set. More conveniently andin most cases the significance measure is a type of numerical quantityassigned to a partition of a composition. Therefore significancemeasures are functions of OSs and one or more of other relatedmathematical objects, wherein a mathematical object can, for instance,be a mathematical object containing information of participations of OSsin each other, whose values are used in the decisions about theconstituent OSs of a composition.

6. Summarization: is a process of selecting one or more OS from one ormore sets of OSs according to predetermined criteria with or without thehelp of value significance and ranking metric/s. The selection orfiltering of one or more OS from a set of OSs is usually done for thepurposes of representation of a body of data by a summary as anindicative of that body. Specifically, therefore, in this disclosuresearching through a set of partitions or compositions, and showing thesearch results according to the predetermined criteria is considered aform of summarization. In this view finding an answer to a query, e.g.question answering, or finding a composition related or similar to aninput composition etc. are also a form of searching through a set ofpartitions and therefore are a form of summarization according to thegiven definitions here.

7. Subject matter: generally is an ontological subject or a compositionitself. Therefore subject matters and OSs have in principal the samecharacteristics and are not distinguishable from each other. Yet lessgenerally and bit more specifically a subject matter (SM), in thepreferred exemplary embodiments of this application, is a word orcombination of a word that shows a repeated pattern in many documentsand people or some groups of people come to recognize that word orcombinatory phrase. Nouns and noun phrases, verbs and verb phrases, withor without adjectives, are examples of subject matters. For instance theword “writing” could be a subject matter, and the phrase “Good Writing”is also a subject matter. A subject matter can also be a sentence or anycombination of number of sentences. They are mostly related, but notlimited, to nouns, noun phrases, entities, and things, real orimaginary. But preferably almost most of the time is a keyword or set ofkeywords or topic or a title of interest.

8. Body of Knowledge: is a composition or set of compositions availableor assembled from different sources. The body of knowledge can berelated to one or more subject matter or just a free or randomcollection of compositions. The “Body of Knowledge” may be abbreviatedfrom time to time as BOK in this application. The BOK can furtherinclude compositions of different forms for instance one part of anexemplary BOK can be a text and another part contains video, or picture,or a genetic code.

9. The usage of quotation marks “ ”: throughout the disclosure severalcompound names of variable, functions and mathematical objects (such as“participation matrix”, “conditional occurrence probability” and thelike) will be introduced that once or more is being placed between thequotation marks (“ ”) for identifying them as one object and must not beinterpreted as being a direct quote from the literatures outside thisdisclosure (except the incorporated referenced patent applications).Furthermore the term “module” in this application means any part,section and/or piece/s of codes of a computer executable instructionprogram. Additionally the term “computer-readable storage medium” refersto all types of non-transitory computer readable media such as magneticcassettes, flash memory cards, digital video discs, random accessmemories (RAMs), Bernoulli cartridges, read only memories (ROMs) and thelike, with the sole exception being a transitory, propagating signal.”

Now the invention is disclosed in details in reference to theaccompanying figures and exemplary cases and embodiments in thefollowing sub sections.

II-I PARTICIPATION MATRIX BUILDING FOR A COMPOSITION

Assuming we have an input composition of ontological subjects, e.g. aninput text, the Participation Matrix (PM) is a matrix indicating theparticipation of a number of the ontological subjects in a number ofpartitions of the composition. In other words in terms of ourdefinitions, PM indicate the participation of one or more lower order OSinto one or more OS of higher or the same order. PM is the mostimportant array of data in this disclosure containing the rawinformation from which many other important functions, information,features, and desirable parameters are extracted. Without intending anylimitation on the value of PM entries, in the preferred embodimentsthroughout most of this disclosure (unless stated otherwise) the PM is abinary matrix having entries of one or zero and is built for acomposition or a set of compositions as the following:

-   -   1. break the composition to a desired number of partitions. For        example, for a text document we can break the documents into        chapters, pages, paragraphs, lines, and/or sentences, words        etc.,    -   2. identify the desired form, number, and order of the        ontological subject of the composition by appropriate method        such as parsing a text documents into its constituent words and        phrases, sentences, etc.,    -   3. select a desired N number of OSs of order k and a desired M        number of OSs of order l (these OSs are usually the partitions        of the composition from the step 1) existing in the composition,        according to certain predefined criteria, and;    -   4. construct a N×M matrix in which the ith raw (R_(i)) is a        vector, with dimension M, indicating the presence of the ith OS        of order k, (often extracted from the composition under        investigation), in the OSs of order l, (often extracted from the        same or another composition under investigation), by having a        nonzero value, and not present by having the value of zero. In        the exemplary embodiments of this disclosure usually the nonzero        value is one (i.e. making the vector R_(i) a binary) for ease of        explanation. However all the formulations and calculations can        still be followed by those skilled in the art by placing any        other desired nonzero value to show the presence of OSs of order        k in the OSs of order l.”

In the present application this matrix is called the “ParticipationMatrix of the order kl” (PM^(kl)) which can be shown as:

$\begin{matrix}{{{OS}_{1}^{l}\mspace{14mu} \ldots \mspace{14mu} {OS}_{M}^{l}}{{PM}^{kl} = {\begin{matrix}{OS}_{1}^{k} \\\vdots \\{OS}_{N}^{k}\end{matrix}\begin{pmatrix}{pm}_{11}^{kl} & \ldots & {pm}_{1M}^{kl} \\\vdots & \ddots & \vdots \\{pm}_{N\; 1}^{kl} & \ldots & {pm}_{NM}^{kl}\end{pmatrix}}}} & (1)\end{matrix}$

where OS_(i) ^(l) is the ith OS of the lth order, OS_(i) ^(k) is the ithOS of the kth order, extracted from the composition, and PM_(ij) ^(kl)=1if OS_(i) ^(k) have participated, i.e. is a member, in the OS_(j) ^(l)and 0 otherwise.

The participating matrix of order lk, i.e. PM^(lk), can also be definedwhich is simply the transpose of PM^(kl) whose elements are given by:

PM _(ij) ^(lk) =PM _(ji) ^(kl)  (2).

Accordingly without limiting the scope of invention, the description isgiven by exemplary embodiments using only the general participationmatrix of the order kl, i.e the PM^(kl).

One of the advantage and benefit of transforming the information of acomposition into participation matrices is that once we attributesomething to one of the OSs then we can evaluate the measures of thatattributes for the other order OSs using the PMs.

Further, in the incorporated patent application Ser. No. 12/939,112, nowU.S. Pat. No. 8,401,980, the applicant has defined the associationstrength of each two OSs as a function of their co-occurrence in thecomposition, or in the partitions of the composition, and theprobability of occurrences of the OSs, as recited here again in thesections II-II and II-III.

II-II—VALUE EVALUATION OF THE ONTOLOGICAL SUBJECTS

After having constructed the PM^(kl) the applicant now launch to explainthe methods of evaluating the “value significances” of the ontologicalsubjects of the compositions. As noticed and suggested before, one ofthe advantages and benefits of transforming the information of acomposition into participation matrices is that once we attributesomething to one of the OSs then we can evaluate the merit of the otherOSs in regards to that attribute with different orders using the PMs.For instance, if we find words of particular importance in a compositionthen we can readily find the most important sentences of the compositionwherein the most important sentences contain the most important words inregards to that particular importance measure.

We explain the method and the algorithm with the step by stepformulations that is easy to implement by those of ordinary skilled inthe art and by employing computer programming languages and computerhardware systems that can be optimized to perform the algorithmefficiently and produce useful outputs for various desired applications.

Here we first concentrate on value significance evolution of OSs ofpredefined order by several exemplary embodiments of the preferredmethods to evaluate the value of an OS of the predetermined order withina same order set of OSs of the composition.

Referring here to the FIG. 1 of the incorporated reference, the patentapplication 12/939,112 now U.S. Pat. No. 8,401,980 B2, we start with onedefinition for association of two or more OSs of a composition to eachother and show how to evaluate the strength of the association betweeneach two OSs of composition. In said FIG. 1 the “association strength”of each two OSs has been defined as a function of their co-occurrence inthe composition or in the partitions of the composition, and theprobability of occurrences of each one of them.

FIG. 1 of the incorporated reference, the patent application 12/939,112now U.S. Pat. No. 8,401,980 B2, shows the concept and rational of thisdefinition for association strength according to this disclosure. Thelarger and thicker elliptical shapes are indicative of the probabilityof occurrences of OS_(i) ^(k) and OS_(j) ^(k) in the composition, thatcan be driven from the data of PM^(kl), and wherein the small circlesinside the area is representing the OS^(l) s of the composition. Theoverlap area shows the common OS^(l) between the OS_(i) ^(k) and OS_(j)^(k) in which they have co-occurred, i.e. those partitions of thecomposition that include both OS_(i) ^(k) and OS_(j) ^(k). Theco-occurrence number is shown by com_(ij) ^(k|l) which is an element ofthe “Co-Occurrence Matrix (COM)” (as will be introduced later) andessentially showing that how many times OS_(i) ^(k) and OS_(j) ^(k) hasparticipated jointly into the OSs of the order l of the composition.From PM^(kl) we can arrive at the CO-Occurrence Matrix COM^(k|l) for OSsof the same order as follow:

COM ^(k|l) =PM ^(kl)*(PM ^(kl)),  R1-(3),

where the “′” and “*” show the matrix transposition and multiplicationoperation respectively. The COM is a N×N square matrix. This is theco-occurrences of the ontological subjects of order k in the partitions(ontological subjects of order l) within the composition and is oneindication of the association of OSs of order k obtained from theirpattern of participations in the OSs of order l of the composition.

Having calculated the COM^(k|l) we then define the association strengthbetween OS_(j) ^(k) and OS_(i) ^(k) as shown in FIG. 1 of theincorporated reference, the patent application 12/755,415 now U.S. Pat.No. 8,612,445. The association strengths play an important role in thevalue significance evaluation of OSs of the compositions and, in fact,can be shown as entries of a new matrix called here the “AssociationStrength Matrix (ASM^(k|l))” whose entries will be defined to show theconcept and rational of association strength according to one exemplaryembodiment of the invention as the following:

$\begin{matrix}{{{asm}_{ji}^{k|l} = {{c\frac{{com}_{ij}^{k|l}}{\left( \frac{{iop}_{j}^{k|l}}{{iop}_{i}^{k|l}} \right)}} = {c\frac{{com}_{ij}^{k|l} \cdot {iop}_{i}^{k|l}}{{iop}_{j}^{k|l}}}}},i,{j = {1\mspace{14mu} \ldots \mspace{14mu} N}},} & {{R1}\text{-}(4)}\end{matrix}$

where c is a predefined constant, a scaling or normalization function,or a predefined function of other variables in Eq. R1-4, com_(ij) ^(k|l)are the individual entries of the COM^(k|l) showing the co-occurrence ofthe OS_(i) ^(k) and OS_(j) ^(k) in the partitions, and the iop_(i)^(k|l) and iop_(j) ^(k|l) are the “independent occurrence probability”of OS_(i) ^(j) and OS_(j) ^(k) in the partitions respectively, whereinthe occurrence is happening in the partitions that are OSs of order l.However in this exemplary case we conveniently can consider the casewhere c=1 as shown in FIG. 1 of the incorporated reference, the patentapplication 12/755,415 now U.S. Pat. No. 8,612,445. The probability ofindependent occurrence in a partition is the “Frequency of Occurrences”,i.e. the number of times an OS^(k) has been appeared in the compositionor its partitions, divided by the total possible number of occurrencesof that OS, i.e. the number of partitions when we do not considerrepeated occurrences of an OS^(k) in any partitions which is the case inthis exemplary description.The frequency of occurrences can be obtained by counting the occurrencesof OSs of the particular order, e.g. counting the appearances ofparticular word in the text or counting its total occurrences in thepartitions, or more conveniently be obtained from the COM^(k|l). The“Frequency of Occurrences” of OS_(i) ^(k) is called here FO_(i) ^(k|l)and can be given by:

FO _(i) ^(k|l) =com _(ii) ^(k|l).  R1-(5)

which is basically the elements on the main diagonal of the COM^(k|l).The “Independent Occurrence Probability” (IOP) in the partitions (usedin Eq. R1-4), therefore, can be given by:

$\begin{matrix}{{{iop}_{i}^{k|l} = \frac{{FO}_{i}^{k|l}}{M}},{i = {1\mspace{14mu} \ldots \mspace{14mu} {N.}}}} & {{R1}\text{-}(6)}\end{matrix}$

Introducing quantities from Eq. R1-5, and 6 into Eq. R1-,4 theassociation strength therefore can be calculated. In a particular case,it can be seen that in Eq. R1-4, the association strength measure ofeach OS with itself is proportional to its frequency of occurrence. Thatis Eq. R1-4 results in asm_(ii) ^(k|l)=c. FO_(i) ^(k|l). However, inorder to have a normalized value for asm_(ii) ^(k|l), i.e. asm_(ii)^(k|l)=1, then one can use the case where c=1/FO_(i) ^(k|l) in the Eq.R1-4 to have self association strength of normalized to 1. Nevertheless,when c=1 in Eq. R1-4 the results of the association strengthcalculations become much more pronounced and distinguishable making itsuitable to find the true but less obvious associations of an OS.Furthermore, more parameters can be introduced in front of each of thevariables in the equations above to have general enough formulations.However those parameters or more variables have been avoided here toprevent un-necessary complication of the formulations. For example for aparticular OS_(I)

It is important to notice that the association strength defined by Eq.R1-4, is not symmetric and generally asm_(ji) ^(k|l)≠asm_(ij) ^(k|l).One important aspect of the Eq. R1-4 is that in this invention it hasbeen pointed out that associations of OSs of the compositions that haveco-occurred in the partitions are not necessarily symmetric and in factit is noticed in the invention that asymmetric association strength ismore rational and better reflects the actual semantic relationshipsituations of OSs of the composition.

To illustrate further in this matter, Eq. R1-4 basically says that if aless popular OS co-occurred with a highly popular OS then theassociation of less poplar OS to highly popular OS is much stronger thanthe association of a highly popular OS having the same co-occurrenceswith the less popular OS. That make sense, since the popular OSsobviously have many associations and are less strongly bounded to anyoneof them so by observing a high popular OSs one cannot gain much upfrontinformation about the occurrence of less popular OSs. However observingoccurrence of a less popular OSs having strong association to a popularOS can tip the information about the occurrence of the popular OS in thesame partition, e.g. a sentence, of the composition.

A very important, useful, and quick use of Eq. R1-4 is to find the realassociates of a word, e.g. a concept or an entity, from their pattern ofusage in the partitions of textual compositions. Knowing the associatesof words, e.g. finding out the associated entities to a particularentity of interest, can find many applications in the knowledgediscovery and information retrieval. In particular, one application isto quickly get a glance at the context of that concept or entity or thewhole composition under investigation.

In accordance to another aspect of the invention, one can recall fromgraph theories that each matrix can be regarded as an adjacency matrixof a graph or a network. Consequently, FIG. 2 of the incorporatedreference, the patent application 12/755,415 now U.S. Pat. No.8,612,445, shows a graph or a network of OSs of the composition whoseadjacency matrix is the Association Strength Matrix (ASM). As seen thegraph corresponding to the ASM can be shown as a directed and asymmetricgraph or network of OSs. Therefore having the ASM one can represent theinformation of the ASM graphically. On the other hand by having a graphone can transform the information of the graph into an ASM type matrixand use the method and algorithm of this application to evaluate variousvalue significance measures for the nodes of the graph or network.Various other graphs can be depicted and generated for each of thedifferent matrixes introduced herein. FIG. 2 further demonstrate thathow any composition of ontological subjects can be transformed (usingthe disclosed methods and algorithms) to a graph or network similar tothe one shown in FIG. 2 showing the strength of the bounding between thenodes of the graph.

Using the association strength concept one can also quickly find outabout the context of the compositions or visualize the context by makingthe corresponding graphs of associations as shown in FIG. 2 of theincorporated reference, the patent application 12/939,112 now U.S. Pat.No. 8,401,980 B2, here. Furthermore, the association strengths becomeinstrumental for identifying the real associates of any OS within thecomposition. Once the composition is large or consist of very manydocuments one can identify the real associations of any ontologicalsubject of the universe. Such a real association is useful when onewants to research about a subject so that she/he can be guided throughthe associations to gain more prospects and knowledge about a subjectmatter very efficiently. Therefore a user or a client can be efficientlyguided in their research trajectory to gain substantial knowledge asfast as possible. For instance a search engine or a knowledge discoverysystem can provide its clients with the most relevant information onceit has identified the real associations of the client's query, therebyincreasing the relevancy of search results very considerably.

As another example, a service provider providing knowledge discoveryassistance to its clients can look into the subjects having highassociations strength with the subject matter of the client's interest,to give guidance as what other concepts, entities, objects etc. shouldshe/he look into to have deeper understanding of a subject of interestor to collect further compositions and documents to extend the body ofknowledge related to one or more subject matters of her/his/it'sinterest.

According to another aspect of the invention, we also put a value ofsignificance on each OS based on the amount of information that theycontribute to the composition and also by the amount of information thatcomposition is giving about the OSs.

To evaluate the information contribution of each OS we use theinformation about the association strength as being related to theprobability of co-occurrence of each two OSs in the partitions of thecomposition. The probability of occurrence OS_(i) ^(k) after knowing theoccurrence of OS_(j) ^(k) in a partition, e.g. OS^(l), is considered tobe proportional to the association strength of OS_(j) ^(k) to OS_(i)^(k) i.e. the asm_(ji) ^(k|l). Therefore we define yet another functionnamed “Conditional Occurrence Probability (COP^(k|l))” here as beingproportional to asm_(ji) ^(k|l). Hence to have entries of COP^(k|l) asthe following:

cop ^(k|l)(i|j)=p ^(k|l)(OS _(i) ^(k) |OS _(j) ^(k))∝asm _(ji)^(k|l).  R1-(7)

Considering that Σ_(j)iop_(j) ^(k|l), cop^(k|l)(i|j)=iop_(i) ^(k|l)(total conditional probabilities of occurrences of OS_(i) ^(k) in apartition is equal to independent occurrence probability of OS_(i) ^(k)in that partition) we arrive at:

$\begin{matrix}{{{cop}^{k|l}\left( i \middle| l \right)} = \frac{{iop}_{i}^{k|l} \cdot {asm}_{ji}^{k|l}}{\sum\limits_{j}{{iop}_{j}^{k|l} \cdot {asm}_{ji}^{k|l}}}} & {{R1}\text{-}(8)}\end{matrix}$

The matrix cop^(k|l)(i|j) can be made to a row stochastic (assuming thei showing the index of rows) but spars (having many zero entries) and interms of graph theories jargon it could be corresponded to an incompletegraph or network. However if for mathematical or computational reasonsit becomes necessary, it can be made to become a matrix that correspondsto a complete graph (every node in the graph is connected directly toall other nodes) by subtracting an small amount from the non-zeroelements and distribute it into the zero elements so that processing ofthe matrix for further purposes can be performed without mathematicaldifficulties (no division by zero etc.).

Now that we have defined and obtained preliminary mathematical objectsof the invention, we proceed with defining several illustrating butimportant “value significance measures” (VSMs) and comparing them interms of computational complexity and usefulness. Mathematically VSMsare vectors that correspond to a number of OSs of interest in thecomposition. Obviously the first indication of significance of an OS inthe composition is the frequency of occurrence or number of times thatan OS has been appeared in the composition or its partitions. The firstValue Significance Measure of OS_(i) ^(k) which is called VSM1_(i) ^(k)then would be:

VSM1_(i) ^(k|l) =FO _(i) ^(k|l) i=1 . . . N  R1-(9)

This is the simplest and most straightforward measure of significance ofan OS in the composition. However when the composition or collection ofcompositions become large (contain very many OSs) the Frequency ofOccurrences of many of OSs can become very close and therefore noisymaking it not a very suitable measure of intrinsic significances.Specially as we will see in the next section when using this measure ofsignificance to evaluate the value significance of higher order OSs,e.g. VSM1_(i) ^(l|k), the results could become noisy and less useful.That is because the frequency count or Frequency of Occurrence (FO)alone does not carry the information of the usage pattern andco-occurrence patterns of OSs with each other. However for manyapplications this measure of significance could be satisfactoryconsidering the simplicity of the processing.

In accordance with another aspect of the invention, the second measureof significance is defined in terms of the “cumulative associationstrength” of each OS. This measure can carry the important informationabout the usage pattern and co-occurrence patterns of an OS with others.So the second value significance measure VSM2_(i) ^(k) for an OS_(i)^(k) is defined versus the cumulative association strength that here iscalled “Association Significance Number (ASN_(i) ^(k))”, will be:

VSM2_(i) ^(k|l) =ASN _(i) ^(k|l)=Σ_(j) asm _(ji) ^(k|l) i,j=1 . . .N  R1-(10)

The VSM2_(i) ^(k) is much less noisy than VSM1_(i) ^(k) and fairlysimple to calculate. It must be noticed that ASN_(i) ^(k) is anindication of how strong other OSs are associated with OS_(i) ^(k) andnot how strong OS_(i) ^(k) is associated with others. Alternatively itwould be important to know a total quantity for association strength ofan OS_(i) ^(k) to others which is Σ_(j)asm_(ij) ^(k|l) (the differencehere with Eq. R1-10 is in the ij instead of ji in the summation). Thisquantity is also an important measure which shows overall associationstrength of OS_(i) ^(k) with others. The difference of Σ_(j)asm_(ji)^(k|l)−Σ_(j)asm_(ij) ^(k|l) is also an important indication of thesignificance of the OS_(i) ^(k) in the composition. The latter quantityor number shows the net amount of importance of and OS in terms ofassociation strengths exchanges or forces. This quantity can bevisualized by a three dimensional graph representing the quantityΣ_(j)asm_(ji) ^(k|l)−Σ_(j)asm_(ij) ^(k|l). A positive number wouldindicate that other OSs are pushing the OS_(i) ^(k) up and negative willshow that other OSs have to pull the OS_(i) ^(k) up in the threedimensional graph. Those skilled in the art can yet envision othermeasures of importance and parameters for investigation of importance ofan OS in the composition using the concept of association strengths.

As an example of other measures of importance, and in accordance withanother aspect of the invention and as yet another measure of valuesignificance we notice that it would be helpful and important if one canknow the amount of information that an OS is contributing to thecomposition and vice versa. To elaborate further on this valuesignificance measure we notice that it is important if one can know thathow much information the rest of the composition would have gained if anOS has occurred in the composition, and how much information would belost when on OS is removed from the composition. Or saying it in anotherway, how much the composition is giving information about the particularOS and how much that particular OS add to the information of thecomposition. The concept of conditional entropy is proposed and isapplicable here to be used for evaluation of such important valuemeasure. Therefore, we can use the defined conditional occurrenceprobabilities (COP) to define and calculate “Conditional EntropyMeasures (CEMs)” as another value significance measure.

Accordingly, yet a slightly more complicated but useful measure ofsignificance could be sought based on the information contribution ofeach OS_(i) ^(k) or the conditional entropy of OS_(i) ^(k) given therest of OS^(k) s of the composition are known. The third measure ofvalue significance therefore is defined as:

VSM3_(i) ^(k|l) =CEM1_(i) ^(k|l) =H1_(i) ^(k|l) =H _(j)(OS _(i) ^(k) |OS_(j) ^(k))=−Σ_(j) iop _(j) ^(k|l) ·cop ^(k|l)(i|j)log₂(cop^(k|l)(i|j)),i,j=1 . . . N R1-(11)

wherein H_(j) stands for Shannon-defined type entropy that operates on jindex only. In Eq. R1-11 any other basis for logarithm can also be usedand CEM1_(i) ^(k|l) stands for first type “Conditional Entropy Measure”and H1_(i) ^(k|l) is to distinguish the first type entropy according tothe formulations given here (as opposed to the second type entropy whichis given shortly). This is the average conditional entropy of OS_(i)^(k) over the M partitions given that OS_(i) ^(k|l) has alsoparticipated in the partition. That is every time OS_(i) ^(k) occurs inany partition we gain H bits of information.And in accordance with yet another aspect of the invention another valuesignificance measure is defined as:

VSM4_(i) ^(k|l) =CEM2_(i) ^(k|l) =H2_(i) ^(k|l) =H _(j)(OS _(j) ^(k) |OS_(i) ^(k))=cop ^(k|l)(j|i)log₂(cop ^(k|l)(j|i),i,j=1 . . . N  R1-(12)

where H_(j) stands for Shannon-defined type entropy that operates on jindex only again, and wherein CEM2_(i) ^(k|l) stands for the second type“Conditional Entropy Measure” and H2_(i) ^(k|l) is to distinguish thesecond type entropy according to the formulations given here. That isthe amount of information we gain any time an OS^(k) other than OS_(i)^(k) occurs in a partition knowing first that OS_(i) ^(k) hasparticipated in the partition.And in accordance with another aspect of the invention yet anotherimportant measure is defined by:

VSM5_(i) ^(k|l) =DCEM _(i) ^(k|l) =CEM1_(i) ^(k|l) −CEM2_(i) ^(k|l)=VSM3_(i) ^(k|l) −VSM4_(i) ^(k|l) ,i=1 . . . N  R1-(13)

where DCEM_(i) ^(k|l) stands for “Differential Conditional EntropyMeasure” of OS_(i) ^(k). The DCEM_(i) ^(k|l) and is a vector having Nelement as is the case for other VSMs. The VSM5^(k|l) is an importantmeasure showing the net amount of entropy or information that each OS iscontributing to or receiving from the composition. Though the total sumof DCEM_(i) ^(k|l) over the index i, is zero but a negative value ofVSM5_(i) ^(k|l) (i.e. DCEM_(i) ^(k|l)) is an indication that thecomposition is about those OSs with negative VSM5^(k|l). The VSM5^(k|l)is much less nosier than the other value significance measures but is ina very good agreement (but not exactly matched) with VSM2^(k|l), i.e.the association significance number (ASN). This is important becausecalculating ASN is less process intensive yet yields a very good resultin accordance with the all important DCEM^(k|l).Also important is that either of CEM1^(k|l) or CEM2^(k|l) can be alsoused (multiplying either one by FO_(i) ^(k|l)) for measuring orevaluating the real information of the composition in terms of bits(wherein bit is a unit of information according to the InformationTheory) which could be considered as yet another measure of valuesignificance for the whole composition or the partitions therein. Forinstance, this measure can be used to evaluate the merits of a documentamong many other similar or any collection of documents. The informationvalue of the OSs or the partitions (by addition the individualinformation of the its constituent OSs) is a very good and familiarmeasure of merit and therefore can be another good quantity as anindication of value significance.Those skilled in the art can use the teachings, concepts, methods andformulations of value significance evaluation of ontological subjectsand the partitions of the composition with various other alterations andfor many applications. We now lunch into describing a number ofexemplary embodiments of implementing the methods and the exemplaryrelated systems of performing the methods and some exemplaryapplications in real life situations.Referring to the FIG. 3 of the incorporated reference, the patentapplication 12/939,112 now U.S. Pat. No. 8,401,980 B2, here, it showsthe block diagram of one basic algorithm of calculating a number of“Value Significance Measures” of the Ontological Subjects of an inputcomposition according to the teachings of the invention. As seen theinput composition is partitioned to a number of desirable partitions andthe lower order OSs of partitions are also extracted and indexed invarious lists of OSs of different orders. In the preferred embodiment ofthe method the partitions would be textual semantics units of differentlengths such as paragraphs, or sentences and chapters. Again here weconsider words and some special characters and symbols as OS order 1,the sentences as OS order 2, the paragraphs as order 3, the sections asOS order 4, and individual documents as OSs of order 5. The inputcomposition can be a single man-made article, a number of documents, ora huge corpus etc. There is no limit on the length of the composition.In an extreme case the input composition might be the whole internetrepositories.Referring to the FIG. 3 of the incorporated reference, the patentapplication the patent application 12/939,112 now U.S. Pat. No.8,401,980 B2, again, it further shows the steps in detail for performingthe methods and the algorithms. After partitioning and extracting theOSs of desired orders, the participation matrix or matrices of desireddimensions and orders are built from which the co-occurrence matrix/s(COM) is built. The Frequency of Occurrence (FO) can be obtained bycounting the OSs while extracting them from the composition or can beobtained from the Co-Occurrence Matrix as indicated in Eq. R1-5, andhence obtaining the Independent Occurrence Probability (IOP) of each OSof the desired order using Eq. R1-6. The first value significancemeasure (VSM1) can then be calculated according to Eq. R1-9. Havingobtained the IOP and COM consequently the “Association Strength Matrix(ASM)” is calculated, (according to Eq. R1-4, and 6) from which thesecond “Value Significance Measure (VSM2)” is obtained using Eq. R1-10.Having ASM, thereafter the “Conditional Occurrence Probability” (COP)for each desirable pairs of OSs are calculated as the entries of the COPmatrix (according to Eq. R1-8). From the Conditional OccurrenceProbability the various combinations of Conditional Entropy Measures,i.e. CEM1, CEM2, DCEM are calculated according to Eq. R1-11, 12, and 13.It is noted that obviously one can select only the desirable OSs of anyorder in building one or more of the matrix objects of the invention.Moreover, one does not need necessarily to calculate all of the VSMsthat have been included in the general algorithm of FIG. 3. FIG. 3 isfor showing one basic exemplary embodiment to illustrate the relationsand the method and algorithm of calculating or evaluating a number ofdistinct VSMs that were disclosed in the description.The interesting and important observation is that the VSM3_(i) ^(1|2),i.e. Conditional Entropy Measure of type 1 (Eq. R1-11), has followed theFrequency of Occurrence (FO) or equivalently the Independent OccurrenceProbability iop_(i) ^(1|2) (Eq. R1-7). That means the behavior of theentropy of OS_(i) ¹ knowing the rest of the composition (Eq. R1-11) isalmost independent of the interrelationships of the OSs in thiscomposition. So knowing the rest of the composition does not affect thegeneral form of the CEM1 from the independent occurring entropy. i.e the−iop_(i) ^(k|l) log₂iop_(i) ^(k|l) which will be quite similar to theIOP or FO.However, the VSM4_(i) ¹, i.e. Conditional Entropy Measure of type 2 (Eq.R1-12), has only followed the Association Strength Number (ASN) andalthough much less noisy but follow the OSs with high IndependentOccurrence Probability iop_(i) ^(1|2) (Eq. R1-7). That means thebehavior of the entropy of the rest of composition knowing the OS_(i) ¹depends on the ASN and strength of the OS_(i) ¹ association (Eq. R1-10or 12) and is in favor of the highly popular OSs. So knowing the highlypopular OSs contribute greatly to the Conditional Entropy Measure oftype 2 (Eq. R1-12).More importantly is the behavior of DCEM, the sum of DCEM is zero but ithas negative values for highly popular (large FO) OSs. That means forthose popular OSs who have many real associates the net entropy orinformation contribution is negative while for the less popular ispositive. An interpretation could be given that all OSs of thecomposition are there to describe and give information about the popularOSs who have real (strong enough) associations. It implies that not allthe popular OSs are important if they do not have real boundedassociates. The real bounding is the reflection of the usage and thepatterns of OSs together in the composition. In other words those OSshaving a high value significance are usually the popular ones but thereverse is not always true.Another explanation is that most popular OSs have many associates orhave co-occurred with many other OSs. Those many other associates havebeen used in the composition to describe the most popular OSs. In otherwords a natural composition (good intentioned composed composition) ismostly about some of the most popular OSs of the composition. So it isnot only the Frequency of Occurrence that count here but the pattern oftheir usage and the strength of their association (which is asymmetric).In conclusion the negative DCEM means other OSs are giving awayinformation about those OSs with negative DCEM. This feature can beuseful for keyword extraction or tagging or classification of documentsbeside that it shows the importance and significance of the OS havingnegative DCEM.Those OSs with the negative DCEM or high ASN can be used forclassification of compositions. However investigation of the differencesin the various VSMs can also reveal the hidden relationships and theirsignificance as well. For example if an OS has gained a betternormalized rank in VSM5_(i) ¹ compared to VSM1_(i) ¹ then that can pointto an important novelty or an important substance matter. Thereforethose experts in the art can yet envision other measures of significanceemploying one or more of these VSMs without departing from scope,concepts and the purpose of this invention.It is also evident that at this stage and in accordance with the methodand using one or more of the participation matrix and/or the consequentmatrices one can still evaluate the significance of the OSs by buildinga graph and calculating the centrality power of each node in the graphby solving the resultant Eigen-value equation of adjacency matrix of thegraph as explained in patent application 12/547,879, now U.S. Pat. No.8,452,725 and the patent application 12/755,415 now U.S. Pat. No.8,612,445 which are incorporated by reference here again.In the FIG. 5 of the incorporated reference. i.e. the patent application12/939,112 now U.S. Pat. No. 8,401,980 B2, the block diagram of onebasic exemplary embodiment in which it demonstrates a method of usingthe association strengths matrix (ASM) to build an Ontological SubjectMap (OSM) or a graph was shown. The map is not only useful for graphicalrepresentation and navigation of an input body of knowledge but also canbe used to evaluate the value significances of the OSs in the graph asexplained in the patent application 12/547,879 entitled “System andMethod of Ontological Subject Mapping for knowledge ProcessingApplications” filed on Aug. 26, 2009 by the same applicant, now U.S.Pat. No. 8,452,725. Utilization of the ASM introduced in thisapplication can result in better justified Ontological Subject Map (OSM)and the resultant calculated significance value of the OSs.

The association matrix could be regarded as the adjacency matrix of anygraphs such as social graphs or any network of anything. For instancethe graphs can be built representing the relations between the conceptsand entities or any other desired set of OSs in a special area ofscience, market, industry or any “body of knowledge”. Thereby the methodbecomes instrumental at identifying the value significance of any entityor concept in that body of knowledge and consequently be employed forbuilding an automatic ontology. The VSM1, 2, . . . 5^(k|l) and othermathematical objects can be very instrumental in knowledge discovery andresearch trajectories prioritizations and ontology building byindicating not only the important concepts, entities, parts, orpartitions of the body of knowledge but also by showing their mostimportant associations.

Various other value significance measures using one or more functions,matrices and variables can still be proposed without departing from thescope, sprit, and the concepts introduced in this invention. Forinstance sum of the elements of the Co-Occurrence Matrix (COM) over therow/column can also be considered as yet another VSM.Nevertheless, one might prefer to use VSM of VSM2, VSM4, or VSM5, forher/his application, which takes into account the usage and pattern ofusage of OSs to each other in the form of the defined exemplaryassociation strength as shown in FIG. 1 of the incorporated reference.i.e. the patent application 12/755,415, now U.S. Pat. No. 8,612,445.The VSM has many useful and important applications, for instance thewords of a composition with high normalized VSM can be used as theautomatic extraction of the keyword and relatedness for thatcomposition. In this way a plurality of compositions and document can beautomatically and much more accurately be indexed under the keywords ina database. Another obvious application is in search engines, webpageretrieval, and many more applications such as marketing, knowledgediscovery, target advertisement, market analysis, market value analysisof economical enterprises and entities, market research related areassuch as market share valuation of products, market volume of theproducts, credit checking, risk management and analysis, automaticcontent composing or generation, summarization, distillation, questionanswering, and many more.In the next section the value significances of the lower order OSs, e.g.words, are used to evaluate the value significances of larger parts ofthe composition e.g. paragraphs, sentences, or documents of a collectionof documents.

II-III—VALUE EVALUATION OF THE HIGHER ORDER ONTOLOGICA SUBJECTS

The value significance of higher order OSs, e.g. order 1 in here, can beevaluated either by direct value significance evaluation similar to thelower order OSs, or can be derived from value significance of theparticipating lower orders into higher order. Conveniently one can usethe VSMx_(i) ^(k|l) (x=1, 2 . . . 5) and the participation matrixPM^(kl) to arrive at the VSMx_(q) ^(l|k) of higher order OSs or thepartition of the composition as the followings:

VSMx _(p) ^(l|k)=Σ_(p) VSMx _(p) ^(k|l) *pm _(pq) ^(kl)  R1-(14)

Eq. R1-(14) can also be written in its matrix form to get the wholevector of value significance measure of OSs of order l|k (l given k).i.e. VSMx^(l|k), as a function of the participation matrix PM^(kl) andthe vector VSMx^(k).Moreover other methods of value significance such as the ones introducedin the patent application 12/939,112 now U.S. Pat. No. 8,401,980 B2, orthe patent application 12/755,415 now U.S. Pat. No. 8,612,445,incorporated as a reference here again, can be employed. Again the mostconvenient one could be:

VSM1^(l|k)=(PM ^(kl))*VSM1^(k|l)=(PM ^(kl))*FO ^(k|l)  R1-(15)

which can be shown to be a special case of Semantic Coverage ExtentNumber (SCEN) introduced in the provisional patent 12/755,415 now U.S.Pat. No. 8,612,445 B2, and incorporated by reference here again, inwhich the similarity matrix is simply SM^(l|k)=(PM^(kl))′*PM^(kl) andSCEN_(i) ^(l|k)=Σ_(j)sm_(ij) ^(l|k) or its mathematical equivalent.Depends on the application, the size of the composition, availableprocessing power and the needed accuracy, one can select to use one ormore of the Value Significance Measures (VSMs) for the desiredapplications.Considering that the motivation for calculating the VSMx^(l|k)x, e.g.VSMx_(i) ^(2|1), is to select the most merit-full partitions from thecomposition for the desired application, e.g. as a distilledrepresentatives of the body of knowledge of the input composition. HenceVSMx are more useful when they are normalized. Therefore slight changein the normalized values of VSMx_(i) ^(k| . . . or l| . . .) can changethe outcome of the applications that uses these values quiteconsiderably.Also important is that either of CEM1^(k|l) or CEM2^(k|l) can be alsoused (after multiplying either one by FO_(i) ^(k|l)) for measuring andevaluating the real information of the composition in terms of bitswhich could be considered as yet another measure of value significancefor the whole composition or the partitions therein.Again depends on the application and the system capability performingthe method and the algorithm one can chose the suitable VSM for thatparticular application.

In regards to VSM evaluation of higher order OSs in general, yet moreconveniently, (also for faster computation), after evaluating the valuesignificance measures of OSs of order l, from the participationinformation contained in PM^(kl), one can proceed to evaluate the ValueSignificance Measures (VSMx) of OSs of other orders, say OSs of theorder l+r and |r|≧0, from the VSMx of the OSs of the order l as thefollowing:

VSMx(OS ^(l+r) |VSMx ^(l|k))=VSMx ^(l+r|(l|k)) =VSMx ^(l|k) ·PM^(l,l+r)  R1-(16).

A composition, e.g. a single document, is entered to the system of FIG.8 of the patent application 12/939,112, now U.S. Pat. No. 8,401,980,which is incorporated by reference here again. The system parse thecomposition, i.e. the document, into words and sentences, and builds theparticipation matrix showing the participation of each of desired wordinto some or all sentences of the composition. Then the system, usingthe algorithm, calculates the COM and ASM and calculates the VSM/s foreach sentence. The summarizer then selects the desired number of thesentences (having the desired range of VSM) to represent to a user asthe essence, or summary, of the input document. One might choose thedifferent ranges or parts of the VSM for other intended applications.

At the same time the method and the system can be employed forclustering partitions of the compositions, e.g. sentence in the abovecase, by simply grouping those partitions having almost the same VSM inthe context of the given input composition.

Again in one particular and important case, consider the inputcomposition to be a large number of documents and the preferred PMmatrix is built for PM^(1,5) (participation of words, k=1, to document,l=5), which is used to subsequently calculate VSMx^(5|1). The resultingVSMx^(5|1) can therefore be used to separate the documents having thehighest merits (e.g. having top substance, most valuable statements,and/or well rounded) within this large collection of the document. Inthis exemplary case, the winner has the highest VSM after a faircompetition, for scoring higher VSMs, with many other documentscontained in the collection. Also shown in the FIG. 8 of the patentapplication 12/939,112, now U.S. Pat. No. 8,401,980, which isincorporated by reference here again, are the data storages storing thecompositions, participation matrixes, the partitions of thecompositions, and the VSMx of the partitions of the composition to beused by other applications, middleware, and/or application servers.Those skilled in the art can store the information of the PMs inequivalent forms or data structures without using the notion of amatrix. For example each raw of the PM can be stored in a dictionary, orthe PM be stored in a list or lists in list, or a hash table, or anyother convenient objects of any computer programming languages such asPython, Java, C, Perl, etc. Such practical implementation strategies canbe devised by various people in different ways. The detaileddescription, herein, therefore uses a straightforward mathematicalnotions and formulas to describe one exemplary way of implementing themethods and should not be interpreted as the only way of formulating theconcepts, algorithms, and the introduced measures. Therefore thepreferred mathematical formulation here should not be regarded as alimitation or constitute restrictions for the scope and sprit of theinvention.”

In summary, one can follow the teachings and the disclosed method of thereferenced patent applications to arrive at evaluating the variousparameters proposed in those applications. In particular the variablesand parameters such as “Semantic Coverage Extent Number”, i.e. the SCENparameter introduced in the incorporated reference patent application12/755,415, now U.S. Pat. No. 8,612,445 B2, and/or the “associationstrength matrix” (ASM), and the different types “value significancemeasures” (VSMs) of lower and higher order ontological subject of agiven corpus or composition which were introduced in the incorporatedreference patent application 12/939,112, now U.S. Pat. No. 8,401,980 B2.

These variables, e.g. SCEN, ASM, different VSMs, are very importantsince they are the measure of the value and significance of the OSs ofthe corpus and can be used to filter, and select the OSs or partitionsof the corpus based on the desired features such as the intrinsic valueof a partition, popularity, authoritativeness, novelty, credibility etc.Effectively these variables and parameters can be viewed as scores ofmerit for the partitions. In the exemplary embodiments of thisdisclosure the intended corpus is a Body Of Knowledge (BOK) that isassembled by the system of this invention in response to a request froma computer program agent or a human user or client. However as will beexplained in one of the embodiment of this invention, the BOK can alsobe provided by the user/client as well.

Body of knowledge (BOK) is a collection of one or more ontologicalsubjects in general which are usually (but not necessarily) are relatedto a subject matter. For instance if one input a subject matter as aquery to a search engine and download all the results given by thesearch engine then this would form a body of knowledge about thatsubject matter. In another instance the BOK might be news feeds about apiece of news from single or different sources. Other examples of a BOKare: a collection of short and/or long messages and comments such as agroup of twitter messages, the visitor's comments to a blog, the contentof several books related to a subject matter, a collection of researchpapers, a collection or group of patent disclosures, or a group ofmovies or multimedia content. Obviously the largest BOK would be thewhole stored contents over the internet.

Participation matrix, or any other objects of this invention, can bestored numerically or by any other programming language objects such asdictionaries, lists, list of lists, cell arrays, databases or any arrayof data etc. which are essentially different representation forms of thedata contained in the PM/s. It is apparent to those skilled in the artthat the formulations, mathematical objects and the described methodscan be implemented in various ways using different computer programminglanguages or software packages that are suitable to perform the methodsand the calculations.

Moreover storage of any of the objects and arrays of data and thecalculations needed to implemented the methods and the systems of thisinvention can be done through localized computing and storage mediafacilities or be distributed over a distributed computer facility orfacilities, distributed databases, file systems, parallel computingfacilities, distributed hardware nodes, distributed storage hubs,distributed data warehouses, distributed processing, cluster computing,storage networks, and in general any type of computing architectures,communication networks, storage networks and facilities capable ofimplementing the methods and the systems of this invention. In fact thewhole system and method can be implemented and performed bygeographically distant computer environments wherein one or more of thedata objects and/or one or more of the operation and functions is storedor performed or processed in a geographically different location fromother parts storing or performing or processing one or more of the dataobjects and/or one or more of the operations or functions of thisdisclosure.

The invention is now further disclosed in details in reference to theaccompanying figures and exemplary cases and embodiments in thefollowing sub sections.

The proposed system disclosed in this invention is designed as a tooland environment for assisting clients and users of information andknowledge to quickly reach at the part of the knowledge that they arelooking for or discovering new knowledge about one or more ontologicalsubjects of the universe. The system itself is an active participant ofthe Interactive/Social Knowledge Discovery sessions (ISKDS) andfurthermore it is intended to be easier and effective to use, more funand incentive for client and users, than the current systems and methodsof knowledge retrievals and discoveries.

Referring to FIG. 1, there is shown one brief and simplified schematicblock diagram of the system of “Interactive Knowledge Discovery Session”or as we called here on IKDS. We first explain the Interactive part ofthe invention and later launch into explaining the Interactive and/orSocial part of the invention. The system consists of computer hardwareand programs to perform the method and algorithm disclosed in the patentapplication Ser. Nos. 12/755,415 and 12/939,112, now U.S. Pat. Nos.8,612,445 B2 and 8,401,980 B2 respectively, and incorporated asreferences sin this application, to evaluate the value significance ofontological subjects.

As shown in FIG. 1, the system will receive a query that ask orintroduce a subject matter for exploration. The system will assemble abody of knowledge (BOK) related to that subject matter. The BOK or thecorpus then is partitioned to the desired partitions, lists of OSs ofdifferent orders are obtained or built, the PMs are built and evaluate,or become ready to evaluate on demand, the semantic significance and/orvalue significance measures (VSMs) of the OSs and the partitionsaccording to the teachings and explanation in the above referencedpatent applications. Participation matrices (PMs) carry the informationof participation of ontological subjects in the same or higher orderontological subjects. The VSMs of the OSs (i.e the partitions) ofdifferent order can be evaluated for the whole OSs of the same orderregardless of their constituent lower order OSs or can be evaluated forthose selected OSs containing the main subject matter of the BOK or anyother subject matter, which is existed in the BOK, in demand. Havingassembled the BOK and built the PMs and having evaluated the one or moreVSMs (or the SCEN value from the patent application 12/755,415) of thepartitions, then system will get back at the client with several optionfor displaying the results or the most important pieces of the knowledgerelated to one or more input subject matters entered to the system bythe user/s. The output knowledge are then represented and displayed byvarious optional methods. As seen in FIG. 1, after getting the list ofpartitions having scored the predefined VSMs types and levels, there isanother selection and editorial block that make the additional editorialon the winner partitions to make them more suitable and readable by theclient. The extra editorial and further selection process before sendingthe response back to the client is not a necessity of the process but itwill ensure a better result and quality.

Referring to FIG. 1 again: as seen, the system starts the interactivesession and provides services and the responses according to the clientrequested subject matter and the mode of services. For instance, theclient can request a concise essence or summary of the knowledge about asubject matter found in the BOK. Alternatively, the client may ask abouta well composed content using the techniques and method of automaticcontent generation made from the BOK according to, for instance, thepatent application 12/946,838 entitled “Automatic Content CompositionGeneration”, now U.S. Pat. No. 8,560,599 B2, by the same applicant.

The client can also request a list of documents based on the value andrelevancy to the subject matter based on one or more of the SCEN(application Ser. No. 12/755,415) or VSMs (application Ser. No.12/939,112) measures that can be used as merit measures to sort thedocument based on their overall intrinsic value, substance, novelty,authoritativeness, or any other desired value or merit measure, etc., inthe collected sets of the documents in the BOK.

More importantly as shown in the FIG. 1, the client can ask about othersubject matters and the content of the assembled BOK of the main subjectmatter of exploration. As will be explained in FIG. 3 a,b and 4 a-c, thesystem provides user interfaces that a client can navigate and identifythe most important or the strongest associates of the main subjectmatter in the context of the BOK and request about the information orthe knowledge expressing the relationships between two or more of thesubject matters from the BOK. The answer in this form again would be thepartitions of the BOK that contain the desired subject matter/s and havethe predetermined range of value significance measures (VSMs). Thesystem has the option to use one or more measures of the VSMs. Theclient and users are provided with visually pleasing graphic userinterfaces and button and icons so that they can select their desiredmode of service e.g.:

-   -   1. a multimedia summary,    -   2. bulleted summary,    -   3. dense summary,    -   4. query specific summary from the BOK,    -   5. graphs of associated subject matters and or ontological        subject maps (OSM),    -   6. in demand, or query-based, automatic content composing,    -   7. content containing two or more subject matters,    -   8. answers to questions or question answering,    -   9. list of the highest value or highest relevancy documents from        the BOK,    -   10. novelty detection or novel information about the queried        subject matter in the context of the BOK,    -   11. query suggestion, idea and question proposition and/or        research guidance; and    -   12. any combination of the above services.

Obliviously the system can have a default mode of responserepresentation from the list above or any other way desired. These listsof services are just few exemplary modes of services for illustrationand explanation only. Those skilled in the art can envision variousother modes of services and response using the main teaching of theinvention in regards to providing interactive environment with thecomputer implemented systems and obtaining relevant responses usingsuitable methods such as one or more of the methods disclosed in thisinvention or the reference applications which are incorporated herein.

The results of the service and system can be displayed on any desirabledisplay apparatus and particularly electric display devices such ascomputer monitor, CRT or LCD, plasma based, laser displays, projectiondevices, touch sensitive displays or touch screen displays, projectors,and the like. Particularly, those displays that, either by way ofsoftware or hardware, are able to react to a user's input or impression,such as pointing and clicking on pixels graphically, or by touching orreading user's expression, voice commands, motions, thought etc.Furthermore, the display devices also mean any portable device having adisplay such as mobile devices, portable and mobile projectors,implanted projectors, laptops and the likes.

Referring to FIG. 2 a now, one of the above exemplary services isfurther illustrated. FIG. 2 a shows one exemplary way of displaying andpresenting the most significant pieces of knowledge related to queriedsubject matter in the form of bulleted or a short list concisestatements (sentences, paragraphs) found in the BOK which scored thedesired ranges of Value significance Measures (VSMs) of the desiredaspect of the value significance. The desired range simply can mean thepartitions that scored the highest VSM1.

The partitions with high VSM/VSMs containing the subject matter orrepresenting the essence of the BOK are usually the most credible piecesof information found in the BOK and having higher relevance and richsemantics conveying often an important fact or important attributes ofthe subject matter. That is because they have either the highestsemantic coverage (e.g. SCEN or VSM1) or containing the most informativecontributive ontological subjects of the corpuses (e.g. having highVSM2, 3, 4, or 5, etc. and/or a predetermined function of theseparameters).

FIG. 2 b shows another option that the summary is presented in more thanone pages with the user interface icons for the user to go back andforth within the presentation of the BOK in the form of a bulleted highvaluable partitions of the BOK that instantly demonstrate the context ofthe BOK. Depicted also in FIG. 2 b, are the clickable or point-ableicons displaying other optional services to the viewer of the display tochose from.

Alternatively the results can be a summarized essence of the BOK or ingeneral or more specifically about the main subject matter by includinga desired number of highest valuable partitions and or the most novelpartitions of the BOK in the results of the interactive session.

One import and very instrumental version of displaying the most valuablepartition of the BOK is to display the partitions of the BOK that havethe highest density value (e.g. highest value per symbol, per bits,highest value per character, or highest value per word). Following thenotations, variables, formulations and the methods disclosed in thepatent application 12/939,112 we define the density value significancemeasure as the following:

$\begin{matrix}{{{DVSM}\; x_{i}^{k|l}} = \frac{{VSM}\; x_{i}^{k|l}}{{Len}\left( {OS}_{i}^{k} \right)}} & (3)\end{matrix}$

where DVSMx_(i) ^(k|l) is the density of Value Significance Measure(VSM) of type x (x=1, 2, 3, . . . ) of the ith Ontological Subject oforder k (i.e. OS_(i) ^(k)), and the len is indicative of length of theOS_(i) ^(k) such as for example the total number of characters or thetotal number of words in sentence or a paragraph, document etc, or anyother desirable measure of length (e.g Euclidean length or distance,Hilbert Space norm, etc).

This measure usually gives the means to select and filter the shorteststatements having high value significance (according to at least onesignificance aspect) in the BOK which is very instrumental in obtainingthe essence of a BOK and quickly find a clue about the context of theBOK.

In the exemplary embodiments of FIGS. 2 a and 2 b, the system alsoprovides another service by the ISKDS system in which after the systemreads and organize the BOK and analyzes the content and evaluate thevalues and associations of ontological subjects contained in the BOK,the system become ready to answer question/s in the context of the BOK.When the client ask a question the ISKDS system will find the partitionwith the highest value significance of the BOK that express a fact orexplain about the ontological subjects of the question. That is fairlystraight forward for the ISKDS system since the system has all types ofparticipation matrices and knows which OS has participated in which ofother partitions (or ontological subjects) and can easily find thepartitions that have a mention of the ontological subjects or theirassociates in the question. Usually the partitions that have the highestVSM then provide the best possible answer in the context and the contentof the assembled or the given BOK. Also usually the larger the BOK thebetter and more accurate the response and answers would be, not only forthe question answering session but also for all other services thatISKDS system will provide to users as will be described throughout thisdisclosure.

Referring to FIGS. 3 a and 3 b now, it shows other further exemplaryways of representing the context of the BOK at a glance as an option inthe interactive knowledge discovery session. These embodiments show thegraphical semantic representations of a subject matter in the context ofthe assembled BOK at a glance. In fact, in these embodiments the systemwill provide a backbone graph indicating the relationships between theconcepts and entities of the BOK and therefore visualizes the truecontext of the BOK and consequently reveals the context of the universeof the body of knowledge.

As seen in these optional embodiments the most important associates ofthe main subject matters, and their own associates, are shown as a nodein a graph that shows their connection and their importance. The indicesof the associated subject matter are configured in a way to show theirassociation route through their parents' nodes up to the main subjectmatter of graph which is shown by SM0 in FIGS. 3 a and 3 b. Forinstance, the node SM012 means this node is representative of a subjectmatter (i.e. an OS) that is the second associate of SM01 which itself isthe first associate subject matter of the main subject matter SM0. Theorders, i.e. the first and the second etc., are based on predefinedcriteria but, for instance, and usually they are ordered based on theirstrength of associations to their parents. Of course in the actualdisplay the actual subject matter, e.g. the name of the entity, concept,picture, symbol etc, will be or can be displayed.

FIG. 3 a shows the graph in the form of hierarchical tree while the inFIG. 3 b the graph is basically free form but can be viewed as thoughthe associates at each level are distributed along a circle co-centeredby the SM0. FIG. 3 b, make a better use of the display space and perhapsmore pleasing while the 3a shows hierarchy of associations in a treelike form. One can select either representation depiction or anotherways of graph representation without departing from the scope of showingthe association of subject matter within the context and contents of theBOK. The semantic graphs of 3a and 3b can be strongly connected (manynodes are connected to each other) or uniquely contented which meanshaving been connected to only higher level parent as is the case in FIG.3 a and FIG. 3 b. In the extreme case the graph can display all thelower order OSs, e.g. the words that have been found in the BOK, as anode and show all the connections using for instance the co-occurrencematrix as an adjacency matrix of the graph.

Referring now to FIGS. 4 a, 4 b, and 4 c: these embodiments are similarto the exemplary embodiment of graphical semantic representation of BOKas FIG. 3 a, visually showing the context of the BOK, whereas there isalso provided graphical interfaces that facilitate the exploration bypointing and clicking on the nodes or the edges to see the relationbetween each two or more nodes.

As shown in FIG. 4 a, user interface is provided along with the semanticgraph of the BOK that visually displaying the context of the BOK inwhich when a user put or hover a cursor on any edge between any twonodes the system shows at least one statement that expresses therelationships of the two nodes.

In FIG. 4 b, user is further provided with graphical capabilities inwhich user can identifies any two nodes (which might not be directlyconnected to each other in the graph) and get the expression about therelationships of the two identified nodes (if there exist suchexplicit/implicit expression/s in the BOK). As shown in this exemplarydepiction the user can point on one and click on it and then point onthe second node and click again whereupon the system displays apartition of the BOK that contains the respective OSs of the identifiednodes and having a high value significance calculated by one or more orcombination of VSMs calculated from the methods disclosed in patentapplication Ser. No. 12/939,112 or 12/755,415. For instance theexpressed partition of the BOK can be selected from those sentences thatcontain both nodes and have high or highest value significance or havinga high or highest dense value significance as given by Eq. 3.

In FIG. 4 c: the system provides the graphical means for a user toidentify an area, that the user likes to know more about, by drawing aboundary of an area on the graphical map that covers one or more of thenodes in the map. The system therefore will display the partitions ofthe BOK, e.g. sentences or paragraphs that contains one or more of thenodes or any combination of them.

In these embodiments (FIGS. 4 a, b, and c) the system can furtherprovide other options such as providing a whole composed content (e.g. amultimedia content) about the selected nodes that demonstrate a highvalue content generated from partitions of the BOK.

In FIG. 5, there is shown a graphical representation of the context ofthe BOK similar to FIG. 3 a in which the geometrical length of the edgesbetween nodes is an indication of their association strength. Thegraphical representation is displayed by selecting the desired number ofassociates of one or more first level nodes, i.e. subject matters, andfor each node and its associate calculate the normalized distance whichmight be given as the following:

$\begin{matrix}{{r_{ji}^{k} \propto {\frac{\max \left( {asm}_{qi}^{k} \right)}{{asm}_{ji}^{k}}\mspace{14mu} {and}\mspace{14mu} q}} \in \left\lbrack {0,1,{2\mspace{14mu} \ldots \mspace{14mu} N}} \right\rbrack} & (4)\end{matrix}$

where r_(ji) ^(k) is the distance between node j and node i in the graphand in fact is inversely proportional to the normalized AssociatingStrength of the OS_(j) ^(k) to OS_(i) ^(k) (e.g. normalized versus thehighest strength associates of the OS_(i) ^(k)), and asm_(ji) ^(k) isthe association strength OS_(j) ^(k) to OS_(i) ^(k) which is an elementof the Association Strength Matrix (ASM) as defined in, for example, byEq. R1-(4).

As seen from FIGS. 3, 4 and 5, for simplicity and clarity we onlyconnects the nodes to its immediately above parent subject matters andeach node or subject matter only connects to a parent and it ownassociates.

Particularly the embodiment of graph shown in FIG. 5 is very importantsince not only the associates of a chosen subject matter (i.e. the mainselected subject matter from the BOK which is usually depicted in thefirst top level) are mapped and easily convey the context of the BOK tothe user but also shows the importance of each subject matter to themain subject matter quite geometrically. The farthest the node is fromone another (in each branch) the less strong the bond or theirassociation is.

These figures are few of the possible ways of representing the essenceand context of a subject matter, using the significance valueevaluation, in order to facilitate the interactive searching orknowledge discovery session. However other forms of representations andmore options or combination of services can be devised without departingfrom the goal and sprit of these depiction which is to quickly andconveniently give a user or a client the most important knowledge abouta subject matter to a user and assist him/her in exploring for moreknowledge or discovering new or less known knowledge.

Referring to FIG. 6 now, there is provided an interactive searchingservice that once a user quires the systems about a subject matter theuser or the client is guided to an open session that is shared withother users or clients that were looking for knowledge about the samesubject matter, and the new user can quickly get an update on the latestfindings and the best pieces of information or knowledge found in therespective BOK of the subject matter. The new participant therefore canalso join the interactive and social knowledge discovery session andstart to gain instant updated knowledge or contribute to the BOK of thatsession. However since the system is capable of interacting with theuser the system itself can be viewed as an active participant andtherefore the social interactive knowledge discovery session can alwaysbe formed even if there is only one human participant. Although some ofthe participants might be software agents that are looking to find theinformation for their own clients.

This embodiment is very instrumental for faster knowledge finding anddiscovery since at any given time there are a large number of people whoare querying search engine about the same subject matter. Thisconfiguration will provide a service for general public to share andlearn from each other. Since participants are not necessarily known toeach other the knowledge shared and found by them, while the socialISKDS is acting as mediator, is highly valuable and credible.

It is noticed that the embodiment of FIG. 6, is very different fromsocial networking providers since firstly the social network providersare not geared toward searching, knowledge finding, discovery, anddistillation. Secondly the participants of social networking in eachgroup or bunch are the same people and most of the time are known toeach other so the ideas and knowledge among them are not totallyuncorrelated and cannot be fresh. Thirdly the participants are requiredto login to a website which results in exclusion of a large number ofcasual surfers who do not want to participate in the social networks butnevertheless need to acquire information and knowledge about arisingsubject matters on the daily basis. And fourthly they do not haveautomatic methods and means to mediate and be able to measure the valueof contents, short or long, and therefore be able to show the mostsignificant pieces of knowledge and information to their client.

This embodiment, FIG. 6, increases the chance of meeting likemindedpeople significantly, yet having very diverse backgrounds, which willresults in much better quality service for knowledge finding andsharing.

Referring to FIG. 6 again, there is shown a schematic of a social searchor “Interactive/Social Knowledge Discovery Session (social IKDS orISKDS)” in which the searcher or client or user are shown graphically asthe graphical object or avatars around a virtual table and discussing acommon subject matter. In this embodiment each user (or as called inthis figure, a participant) sees the most credible and latest discoveredor stated pieces of knowledge about his/her subject of interest whileeach user individually have access and is provided with further servicessuch as the one outlined in the descriptions of the FIGS. 2 to 5. Inthis embodiment a system can be represented as the mediator or as one ofthe participants who have the highest volume of content or knowledgeabout that subject matter of the discussion and knowledge discoverysession.

It is also noticed that all the embodiments and configuration canperform essentially as a search engine that provide various content/spackages in response to a query. For example, when the system providesan answer to a query in the form of a list of ranked webpages based ontheir VSM scores then the service of the system is similar to thecurrent search engines though with different scoring and rankingmethods. Therefore, for instance, a user can query the system as asearch engine and have the option to be directed to the interactivediscussion session related to the queried subject matter like FIG. 6.The system will then present the latest and more valuable partitions ofthe BOK to that point while user can also migrate or demand otherservice introduced in the FIGS. 2 to 5.

In FIG. 7, for instance, there is shown a round table with participantsthat are searching or exploring a knowledge discovery session about asubject matter that is common with other users or participants. In thisembodiment the system announces the latest most valuable knowledge aboutthe subject matter. The most valuable knowledge is obtained byevaluating the VSM of the partitions of the BOK in the one to one ISKDScase or it's users inputs are added to the BOK or the user's input isevaluated in the context of the previously known knowledge contained inthe BOK or simply the most valuable partition is the part that have thehighest consensuses which is equivalent to having a highest VSM or denseVSM.

In FIG. 8, there is shown another embodiment similar to FIGS. 6 and 7 inwhich the option or service is further provided to the participants(e.g. users or clients) for in demand type of information about thesocial interactive knowledge discovery session (ISKDS). For instancebesides seeing the ongoing session and the latest funding about thesubject matter, a participant can privately use other services of thesystem in regards to the subject matter and it's respective BOK. Theparticipant can, for example, order or ask for the maps explained inFIG. 3-5, or ask about the concise essence presentation of the BOKsimilar to FIG. 2, or ask about the summary, or questioning the systemand get the answer back in the form of the most valuable and relevantpart of the BOK.

Meanwhile the system also have the option to display the other ongoingsessions who's subject matter is associated to the subject matter of thecurrent session and a participant can switch to or become a participantto more than one social ISKDS and gain more perspective of the relatedsubject matters of his/her interest. FIG. 9 shows an embodiment that aparticipant can see other ongoing session and the results of theirdiscussion or demand the similar services as the one in FIG. 8 to befetched from another ongoing session which may or may not be related tothe subject of the social ISKDS that the participant is currentlyparticipating. However the system usually can suggest the most closelyrelated and relevant sessions to the client and client can participatein one or more session and request his desired services from the system.The participants may further be notified about important finding/s inthe context of the body of knowledge of the ISKDS after leaving thesession.

The participants not only see and share the latest more credible andmost valuable findings about a subject matter they can also provide aninput and express their conclusion or further reasoning to the systemwhich will become part of the BOK of the subject matter of that socialISKDS and the participant's input can be measured in terms of itscredibility, novelty, and generally one or more aspect of its valuesignificance.

In FIG. 10, the system evaluates the USM of each user's input (which isin the form of short statements, paragraphs, a document or in generalany composition) in the context of the session's BOK and measure thevalue of the user's input. The higher the value the higher the degree ofconsensus of past and present and the assembled BOK about the content ofuser's input. When something novel is expressed about the SM of thesession (i.e. the main SM of the session) it is also evaluated and isplaced at the fresh contents of the session until there is enoughcontent for comparison and to reach to a scored measure based on theusers' input and contribution toward adding new knowledge about thenovel subject matter in the context of the session's BOK or as aseparate independent session. Therefore in FIG. 10 the system isdesigned as such to undertake a contest in order to add and validateexisting and new knowledge about a subject matter. The contest can havea prize or reward in the form of monetary valuable notes or goods orequivalents. The users can submit their content, short and long, andtake part in the contest or get an instant feedback on the quality andsubstance of their composed content in comparison to the knowledge inthe body of the knowledge available for the subject matter/s of thesubmitted content.

The system may further measure the impact of a user's contribution tothe body of knowledge by observing the changes in the value significanceof the partitions of the body of knowledge as a result of one's input.The measure of impact in general can be estimated by a function of thevariations in the value significances of the partitions of the body ofknowledge after a predetermined number of user's input from one or moreuser and/or a predetermined time interval. Such a measure of impact isindicative of the one's contribution importance in terms of changing thecontext of the body of knowledge over time as result of new findingsthat were initiated by one's added input to the body of knowledge ofsession.

The number of participants can be very large and the system provides thelatest findings about the subject matter of the interest to eachparticipant. In this case the system will act as a mediator. Theparticipants can be the registered users competing with each other toprovide a higher value contribution thereby giving the people theincentive and motivation to participate. The system can provide theincentive to the contributing participants in the form of credit ormonetary valuable scores, notes, coupons, etc.

Third party can also provide incentives for knowledge discoverysessions. For instance an enterprise can introduce a prize or incentiveto the contributors of knowledge discovery sessions related to thesubject matters that are important for that enterprise. The system isable to measure the significance of a contribution again using thetechnology and system and method disclosed in this invention and alsofrom the incorporated referenced patent applications.

In another application consider that a user have collected a number ofdocuments and contents and would like to search within that collectionor body of knowledge (BOK). The current keyword searching methods alonewill not work here since the collection might be large and for any givenkeyword, especially for the dominant keywords of the BOK, there will befound many statements or partitions that contain the keyword but mightnot have any real knowledge significance or information value. Thepresented system and method here along with the methods and teachings ofthe referenced patent applications always presents the most significantpartitions of the BOK in response to a query from user for finding theinformation from the BOK. Again the system moreover will provide abackbone graph indicating the relationships between the concepts andentities of the BOK and therefore visualizes the true context of the BOKand therefore the context of the universe of the body of knowledge isrevealed.

In FIG. 11, another exemplary system of ISKDS in which the clientprovides the content or the BOK. Client could assemble a BOK and thenuse the system to start the interactive session services, or provide thedatabases for the system to build the BOK. For instance, a researcher oran enterprise can put some or all of his/it's files or documentstogether and use the ISKDS system to find out the context of hisdocuments, and/or gain knowledge of the whole corpus in a glance or byasking more specific questions from the system to find and become bewareof important subject matters of his/it's data. For instance an attorneycan put all his document related to a case and quickly get a result ofthe most important and valuable partitions and statements of the thislegal corpus or the BOK. However in this case the client might not knowthe subject matter and would like to us the ISKDS system to find out theimportant subject matters of his own data and then dig further insidethese collections to get insight and answers about and from his owndata.

Another usage and application of such embodiments beside individualusers, as an individual researcher or knowledge seeker or student ortrainee, is that large number of people can participate to produce newknowledge or compose a new and more valuable composition. For instanceeditorial articles can be added to the knowledge database. The contentfurther can be shared or published in one of the publishing shops (aswas introduced in the incorporated reference application 12/179,363,filed on Jul. 24, 2008, i.e. the published US patent application US200930030897 filed by the same applicant) or other media.

In FIG. 12, therefore is shown yet another embodiment in which a usercan create his own journal and submit and solicit contents, the systemthen assemble a BOK (with or without the help of the user or otherusers) for that subject matter submitted by the user.

In FIG. 12 users can create their own journal and submitting content/s,the user or the system assembles a BOK for the SM/s of the submittedcontent by the user to be evaluated in terms of its merit in the contextof the BOK related to subject matter. The user can become qualified toestablish an online publishing journal by predetermined criteria orconditions. For example, a user can submit a content of his own and thesystem rank the content against other contents that system can find andassembles from variety of sources such as scientific journal libraries,encyclopedias, internet, and/or by summarizing and evaluating thecomments of other users active in the same field as the submittedcontent, and if the user content value ranks, for instance, in top 10among the documents of the BOK then the user can obtain the privilegesof establishing that journal and enjoy the benefits of the journal suchas having a share of ad revenues or the subscription etc. as long as theuser can maintain the journal competitive or wishes to continue thejournal with the title that come from the main subject matters of thejournal. There could be many sorts of arrangements between the vendorexecuting and implementing the methods of this invention and a user forestablishing a journal. For instance, if the user content rank in topten list of the most valuable contents in the context of the assembledBOK then user have the option to claim that journal (in accordance withthe published patent application US 2009/0030897 disclosures) and enjoysthe benefits of the journal such as ad revenue, paid research etc.However still other people can compete to generate other journals on thesame subject matter if they become qualified (their submitted contentranks top ten in the context of the assembled BOK related to the subjectmatter).

The presented systems and methods in this invention provide services tothe information and knowledge searchers and contributor to interactivelyexplore and find their sought after pieces of knowledge while having theconfidence that the found information or knowledge have a realsignificance value in the body of the knowledge of the subject matter oftheir interests. Also they will be provided with the chance and theservice to interact with other searchers of the same subject matterwhile having a system that mediates the interactive and social knowledgediscovery session by evaluating the significances of the contents in thecontext of existing bodies of knowledge of the subject matter, makingsure that the exchanged knowledge or discovered knowledge has a realsignificance and credibility. Moreover user will achieve his/her goaland perform the searching task at much faster rate leading to muchhigher productivity and efficiency of knowledge works and professionalsas well as general public.

It is apparent to those skilled in the art that such disclosed systemsand methods can be executed and implemented in many different ways andconfigurations and topologies. For example, one or more of the functionscan be executed or performed by different processing units in differentlocations, or in general be scattered around the globe. As an example,in one exemplary implementation of the systems and methods of thisinvention, one computer programming script can run several processingdevices in parallel or in a pipelined manner by executing one functionor computer program and obtaining the results from one computer programand feed them into another computer program that may be executed by aprocessing device in distant location from the other processing device/swherein the processing devices can communicate over a data networkusing, for example, network interfaces or buses, and networking scriptsetc.

A provider of such services, a promoter or a business associate, and/orthe vendor facilitating the exchange of data over the datacommunications networks are considered as the integrator of thedisclosed systems and methods. Therefore from this disclosure point ofview the system can topologically being summarized in the system (evenas simple as a router, a switch or a bridge) that facilitate theexchange of data between the users and at least one of the various partsof the system/s of this invention regardless of the physical locationsof the hardware and the associated operations and apparatuses, e.g. sitehosting, servers, data storages, engines, marketing, accounting,engineering, etc.

Additionally those familiar with the art can yet envision and use themethod and system for many other applications. It is understood that thepreferred or exemplary embodiments and examples described herein aregiven to illustrate the principles of the invention and should not beconstrued as limiting its scope. Various modifications to the specificembodiments could be introduced by those skilled in the art withoutdeparting from the scope and spirit of the invention as set forth in thefollowing claims.

What is claimed is:
 1. A method of interactive and social knowledgediscovery service comprising: receiving one or more inputs from one ormore users; providing, using one or more data processing or computingdevices, an interactive session interface for the one or more users tointeract with a computer program comprising instructions executable byone or more data processing or computing devices, configured, whenexecuted using one or more data processing or computing devices, to:provide an answer or a response respective of the one or more users'input, wherein the answer is composed using one or more partitions orone or more ontological subjects of a body of knowledge having scored apredefined range of value significance according to at least one valuesignificance measure; and providing, using one or more data processingor computing devices, an environment for interaction between saidcomputer program and said one or more users respective of one or moreinputs from the one or more users so as to have an interactive andsocial knowledge discovery session.
 2. The method claim 1, wherein theenvironment for interaction is further configured to represent a user inthe interactive environment by a visual object visible on the display orscreening device of at least one of the user.
 3. The method of claim 1,wherein the environment for interaction is visually displayed on adisplay device or screen as a virtual roundtable conveying a groupdiscussion environment.
 4. The method of claim 1, wherein the one ormore inputs from the one or more users are added to the body ofknowledge.
 5. The method of claim 1, wherein said answer is composedusing association strengths between a plurality of ontological subjectsor partitions of the body of knowledge.
 6. The method of claim 1,wherein said computer program further provides a score of valuesignificance for a user's input using a body of knowledge comprising oneor more inputs from the one or more users and at least one of thepartitions of the body of knowledge of the interactive and socialknowledge discovery session.
 7. The method of claim 1, furthercomprising: providing an option to another user, with interest insimilar or associated subject matters to the ontological subjects of thebody of knowledge of the interactive and social knowledge discoverysession, to join in the interactive and social knowledge discoverysession based on their input.
 8. The method of claim 1, wherein saidcomputer program is further configured to provide a plurality of optionsto a user to obtain one or more content related to the body of knowledgeof the interactive and social knowledge discovery session.
 9. The methodof claim 1, further comprising: enabling the one or more users to editor compose a composition and/or add compositions to the body ofknowledge of the interactive and social knowledge discovery session. 10.The method of claim 1, further comprising: providing a reward to atleast one user whose input has scored a predefined level of valuesignificance, said value significance is a function of one or more valuesignificance measures, in the body of knowledge of the interactive andsocial knowledge discovery session.
 11. The method of claim 1, furthercomprising: measuring impact value of a user's contribution to the bodyof knowledge as a function of the variations in the value significancesof one or more partitions or one or more ontological subjects of thebody of knowledge after a predefined number of the one or more users'input from one or more user and/or a predetermined time interval. 12.The method of claim 1, further comprising: interactively exploringdetails of the body of knowledge wherein a user input further specificquery and obtain at least one respective piece of content in response tothe query.
 13. The method of claim 12, further comprising: finding apreferred research trajectory to a user based on interaction tracks orone or more past inputs of the one or more users.
 14. The method ofclaim 1, further comprising: providing a mediating environment byscoring a user's input, based on at least one value significancemeasure, and demonstrating the significance of the user's input to otherusers of the interactive and social knowledge exploration session.
 15. Amethod of social search and/or social interactive knowledge discoverycomprising: providing, using one or more data processing or computingdevices, a searching utility environment wherein a participant can inputa query or content; creating, using one or more data processing orcomputing devices, an interactive session related to a body of knowledgein response to a participant's input to the searching utility;providing, using one or more data processing or computing devices, asocial and interactive environment for one or more participants whereinat least one participant can input content, and providing or composing acontent, using one or more data processing or computing devices, useableby one or more participants based on one or more input from one or moreparticipants in the session, wherein the content is composed usingpartitions or ontological subjects of a body of knowledge based on theirvalue significances according to at least one value significancemeasure.
 16. The method of claim 15, wherein one of the participants isan intelligent agent that provides dynamic contents in response to aparticipant's input.
 17. The method of claim 16, wherein the agent is acomputer program module having instructions embodied thereon that whenexecuted by a computer system, said computer system having at least oneprocessing device and one or more data communication interfaces, willperform: a. identifying a body of knowledge related to a participant'sinput, b. analyzing the participant's input and providing a respectiveanswer wherein the answer is composed using partitions or ontologicalsubjects of a body of knowledge having scored a predefined range ofvalue significance, said value significance is a function of at leastone value significance measure, and having at last one predefined typeof relationship with the participant's input.
 18. The method of claim17, wherein the agent further scores value significance of the user'sinput, based on at least one value significance measure, using data ofthe body of knowledge.
 19. The method of claim 15, wherein said contentis composed using association strengths between a plurality ofontological subjects or partitions of the body of knowledge.
 20. Themethod of claim 17, wherein the content is shared and/or published byone or more of the participants in a predetermined medium.
 21. Themethod of claim 15, further configured to provide platforms to one ormore participants to establish an online journal and the agent providesassistance to evaluate value of the inputs in respect to the knowledgeexisted in the body of knowledge.
 22. A system of providing at least oneservice over a data network comprising: at least one computer programcomprising programming codes which, when executed by one or more dataprocessing or computing devices, cause to create an interactive sessionenvironment for obtaining an input from a user; at least one firstcomputer program comprising programing codes configured, when executedby one or more data processing or computing devices, to access at leastone content, said at least one content is output of at least one secondcomputer program comprising programing codes which, when executed by oneor more data processing or computing devices, configured to perform: i.accessing at least one body of knowledge, ii. composing an output usingthe ontological subjects or partitions of body of the knowledgeaccording to the user's input and value of at least one measure of valuesignificance of the ontological subjects or the partitions of the bodyof knowledge; and at least one server, comprising one or moreinformation processing or computing device and programing codes imbeddedin one or more no-transitory computing readable media, to respond to theuser's input or send the output over a network.
 23. The system of claim22, wherein the body of knowledge is accessed based on the user's inputor is provided by the user.